Volume 123, Issue 5 p. 2704-2717
Research Article
Free Access

Emergent Behavior of Arctic Precipitation in Response to Enhanced Arctic Warming

Bruce T. Anderson

Corresponding Author

Bruce T. Anderson

Department of Earth and Environment, Boston University, Boston, MA, USA

Correspondence to: B. T. Anderson,

[email protected]

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Nicole Feldl

Nicole Feldl

Department of Earth & Planetary Sciences, University of California, Santa Cruz, CA, USA

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Benjamin R. Lintner

Benjamin R. Lintner

Department of Environmental Sciences, Rutgers, State University of New Jersey, New Brunswick, NJ, USA

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First published: 27 December 2017
Citations: 10

Abstract

Amplified warming of the high latitudes in response to human-induced emissions of greenhouse gases has already been observed in the historical record and is a robust feature evident across a hierarchy of model systems, including the models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main aims of this analysis are to quantify intermodel differences in the Arctic amplification (AA) of the global warming signal in CMIP5 RCP8.5 (Representative Concentration Pathway 8.5) simulations and to diagnose these differences in the context of the energy and water cycles of the region. This diagnosis reveals an emergent behavior between the energetic and hydrometeorological responses of the Arctic to warming: in particular, enhanced AA and its associated reduction in dry static energy convergence is balanced to first order by latent heating via enhanced precipitation. This balance necessitates increasing Arctic precipitation with increasing AA while at the same time constraining the magnitude of that precipitation increase. The sensitivity of the increase, ~1.25 (W/m2)/K (~240 (km3/yr)/K), is evident across a broad range of historical and projected AA values. Accounting for the energetic constraint on Arctic precipitation, as a function of AA, in turn informs understanding of both the sign and magnitude of hydrologic cycle changes that the Arctic may experience.

Key Points

  • Intermodel differences in projected temperature increases are greatest in the polar regions
  • Models with enhanced Arctic amplification (AA) of temperatures also show enhanced Arctic precipitation
  • Enhanced Arctic precipitation's latent heat release serves to balance reduced dry static energy fluxes that accompany enhanced AA

1 Introduction

Although responses of the global climate system to anthropogenic forcing via carbon dioxide and other greenhouse gases are frequently quantified in terms of global mean temperature increases, the response is recognized to be rather spatially heterogeneous (e.g., Hegerl et al., 1996, and references therein). One of the most robust signatures of human-induced global warming is a polar amplification of the overall temperature increase, which was first hypothesized by Arrhenius (1896) and which has recently emerged as a leading indicator of human influence on the climate (e.g., ACIA, 2005; Bekryaev et al., 2010; Gillett et al., 2008; Jeffries et al., 2013; Serreze et al., 2009; Walsh et al., 2011). The presence, magnitude, and extent of this amplified warming of high latitudes in turn have important implications for chemical, biological, and physical systems across the region, particularly in the Arctic. Chemically, Arctic amplification (AA) of the global warming signal is of primary concern because of its potential for releasing vast amounts of organic carbon currently stored within permafrost and on the continental shelves (Archer et al., 2009; DeConto et al., 2012; Schuur et al., 2015). Ecologically, terrestrial and marine biota in the region are highly sensitive to changes in both geographic and temporal characteristics of the resultant climate (Diffenbaugh & Field, 2013; Post et al., 2013; Xu et al., 2013). And physically, polar amplification by definition directly influences meridional temperature gradients both near the surface and aloft (Chung & Räisänen, 2011; Laliberté & Kushner, 2013), which in turn influence the dynamic and thermodynamic state of the atmosphere and underlying ocean (e.g., Hassanzadeh et al., 2014; Held, 1993; Schneider et al., 2010; Shaw et al., 2016). While still a topic of active research (e.g., Cohen et al., 2014; Overland et al., 2016), potential responses include geographic shifts in the position and strength of the midlatitude jet stream and storm tracks (Barnes & Polvani, 2015; Butler et al., 2010; Cattiaux & Cassou, 2013; Rivière, 2011); changes in the frequency and intensity of quasi-stationary, long-lived atmospheric blocks that give rise to hot and cold extremes (Barnes, 2013; Cattiaux et al., 2016; Francis & Vavrus, 2012; Hassanzadeh et al., 2014; Screen & Simmonds, 2013); modifications of the extent and rate of cyclogenesis that determine storm intensity (Inoue & Hori, 2011; Overland & Wang, 2010; Terpstra et al., 2015); and adjustments of the thermal equator and its control on the low-latitude hydrologic cycle (Chiang & Bitz, 2005; Kang et al., 2009; Seo et al., 2014).

Accordingly, amplified warming in the Arctic has the potential to influence regional climates both locally and remotely (Barnes, 2013; Schneider et al., 2015; Screen & Simmonds, 2013; Walsh, 2014). Importantly, the climate response not only involves changes in temperature but also precipitation (Bintanja & Selten, 2014; Deser et al., 2015; Kopec et al., 2016; Park et al., 2013; Semmler et al., 2012). For this reason polar amplification of the global warming signal has been analyzed extensively using simple energy balance models (e.g., Alexeev & Jackson, 2013; Hwang et al., 2011; Lian & Cess, 1977; Roe et al., 2015), atmosphere-only numerical climate models (Deser et al., 2015; Screen et al., 2012), idealized climate models (e.g., aquaplanet simulations—Alexeev et al., 2005; Feldl & Roe, 2013), and fully coupled Earth system models (e.g., Graversen & Wang, 2009; Holland & Bitz, 2003; Pithan & Mauritsen, 2014). However, the magnitude and structure of the amplified warming remains uncertain and estimates differ even when model systems are provided with equivalent trajectories of radiative forcing (Bracegirdle & Stephenson, 2013; Laliberté & Kushner, 2013). Indeed, the leading pattern of intermodel differences in projected temperature increases, after accounting for overall global mean climate sensitivity, shows some of the largest discrepancies in the Arctic regions (cf. Figure 1 below).

Details are in the caption following the image
(a) Intermodel differences in structural surface temperature (sTs) trends regressed on the normalized model weightings for the first principal component (PC) of intermodel sTs trend differences at all grid points. sTs values calculated by first removing temperature trend differences associated with intermodel differences in the models' global mean temperature trend—see text for details. Units—(K). Shading interval given by color bar to the right. (b) Same as Figure 1a except for the first PC of intermodel sTs trend differences for only grid points north or 70°N.

Following from this recognition, our motivating interest here is to isolate intermodel differences in the estimated AA of the overall global mean temperature trend and analyze corresponding intermodel differences in thermodynamic properties of the atmosphere. While numerous studies have addressed this issue (e.g., Bengtsson et al., 2013; Hwang et al., 2011; Kay et al., 2012; Semmler et al., 2012; Serreze et al., 2007), our focus is on the linkages between changing atmospheric energetics and its connection to precipitation. Specifically, based on previous analyses demonstrating high sensitivity of precipitation to changing energetics in the Arctic region (Bintanja & Selten, 2014; Vihma et al., 2016) as well as in the tropics (Chiang & Bitz, 2005; Friedman et al., 2013; Kang et al., 2009) and globally (Allen & Ingram, 2002; Pendergrass & Hartmann, 2014), we hypothesize that intermodel differences in AA provide an energetic constraint on intermodel differences in projected precipitation trends over the Arctic. The rest of this paper is laid out as follows. Section 2 details the data and methods used in the analysis. Results are presented in section 3. A discussion and summary of these results are provided in sections 4 and 5.

2 Data and Methods

For this study, we use data from Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations forced by the RCP8.5 scenario, which imposes an effective 8.5 W/m2 forcing by the year 2100 (Taylor et al., 2012). Output from one ensemble member from each of eighteen (18) models for the period 2006–2100 is used (listed in Table 1). For intercomparison all model data have been linearly interpolated to a common 5° × 5° grid. For all data we calculate grid point trends over the 95 year period 2006–2100 by first calculating running 20 year means (for a given variable) across the full time period. We then fit a linear time trend to the data using least squares regression. Next we take the difference of the first and last value of the linear trend, which represents the difference between the climatological characteristics during the first and last 20 year intervals of the period, after removing high-frequency fluctuations.

Table 1. Information Regarding Model Simulations of Projected Temperatures From the Coupled Model Intercomparison Project 5 (CMIP5) Multimodel Ensemble Used in This Study
Modeling center (or group) Institute ID Model name
Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia CSIRO-BOM ACCESS1.0
Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia CSIRO-BOM ACCESS1.3
Beijing Climate Center, China Meteorological Administration BCC BCC-CSM1.1
Canadian Centre for Climate Modeling and Analysis CCCMA CanESM2
National Center for Atmospheric Research NCAR CCSM4
Community Earth System Model Contributors NSF-DOE-NCAR CESM1(CAM5)
Centro Euro-Mediterraneo per I Cambiamenti Climatici CMCC CMCC-CM
Centre National de Recherches Météorologiques/Centre Européen de Recherche et Formation Avancée en Calcul Scientifique CNRM-CERFACS CNRM-CM5
Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence CSIRO-QCCCE CSIRO-Mk3.6.0
NOAA Geophysical Fluid Dynamics Laboratory NOAA GFDL GFDL-CM3
NASA Goddard Institute for Space Studies NASA GISS GISS-E2-R
Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais)

MOHC

(additional realizations by INPE)

HadGEM2-CC
Institute for Numerical Mathematics INM INM-CM4
Institute Pierre-Simon Laplace IPSL IPSL-CM5A-MR
Atmosphere and Ocean Research Institute (University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology MIROC MIROC5
Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology) MPI-M MPI-ESM-LR
Meteorological Research Institute MRI MRI-CGCM3
Norwegian Climate Centre NCC NorESM1-M

For near-surface temperatures, we calculate two different grid point trend values. The first, Ts, is simply the grid point values as derived above. In addition, we calculate “structural surface temperature” (sTs) trend values, which are designed to represent the structure of grid point variations in surface temperature trends accounting for a given model's global mean temperature increase. To do so, at each grid point we linearly regress the intermodel Ts trend differences against intermodel differences in the models' global mean temperatures; for each model (and grid) we subsequently reconstruct the linear Ts trend map based upon the given model's global mean temperature increase and subtract it from the overall trend map (Anderson et al., 2015).

Following Anderson et al. (2015) and Langenbrunner et al. (2015), we then determine the leading contributors to intermodel differences in sTs trends by performing a “Principal Uncertainty Pattern” analysis. We do so here by applying Empirical Orthogonal Function (EOF) analysis across the “space model” domain using the grid point values of intermodel sTs trend differences. We perform the EOF analysis over both a global domain (as in Anderson et al., 2015) and over an Arctic-only domain, defined here as 70°–90°N. We then calculate weighted composite-mean trends (for a given variable) using the Principal Component (PC) weights for the leading mode; this is done separately for models with positive and negative PC weights. The model weights can also be regressed against intermodel trend differences for a given variable.

3 Results

We first illustrate the leading mode of global intermodel sTs variability, as determined via EOF analysis applied to the global sTs trends (Figure 1a). Overall, this mode is related to intermodel differences in interhemispheric sTs gradient trends principally over the extratropical regions and maximizing in the polar regions of both hemispheres. To further refine our analysis, we apply the same method to the sTs trends north of 70°N (Figure 1b). Importantly, the Northern Hemisphere structure of the leading mode of global sTs trend variability partially represents intermodel variability in the strength of AA of the global warming signal, as captured by the Arctic sTs trend variability (although the Southern Hemisphere structure in the latter contains somewhat enhanced intermodel variability in the tropics and reduced intermodel variability in the subpolar regions).

Considering just the Northern Hemisphere manifestation of the leading mode of Arctic sTs trend variability (Figure 2), the models with positive weightings (Figure 2a) manifest substantial amplification of the global mean signal, by up to 10 K, centered over the Arctic ocean. By contrast, for models with negative weightings (Figure 2b) the amplification of the global mean signal is weaker (~2–4 K) and centered over the Barents Sea. Differencing these two composite values (Figure 2c) suggests a difference in the AA signal by up to 5 K, with enhanced warming extending to the extrapolar regions of North America, Eurasia, and the North Pacific.

Details are in the caption following the image
(a) Weighted composite-mean sTs trends for positive model weightings of the first PC of Arctic intermodel sTs trend differences (K). Shading interval given by top color bar at right. Contour interval as labeled. Black circle indicates Arctic region analyzed throughout. (b) Same as Figure 2a except for negative model weightings of the first PC of Arctic intermodel sTs trend differences (K). (c) Difference between positive and negative composite maps as shown in Figures 2a and 2b. Shading interval given by bottom color bar at right. Contour interval as labeled. (d) sTs trends regressed on the model weightings of the first PC of Arctic intermodel sTs trend differences (K), multiplied by 2.

Given the mathematical construction of sTs, the values shown in Figure 2 represent temperature trends relative to the global mean and as such they reflect the structural signal of intermodel differences in AA. To confirm this, we reconstruct the model-specific sTs values using just the first PC of Arctic intermodel sTs trend differences. We then plot the area-averaged values from 70° to 90°N from each model against a more conventional measure of AA, namely, the difference in the area averaged Ts trend values from 70° to 90°N minus the global mean trend values (Figure 3). As expected, there is a robust relation between the two, although the two metrics do not align perfectly because the conventional AA metric only captures the magnitude of amplified warming in each model while the first PC of Arctic intermodel sTs trend differences also captures the structural representation of that amplification in each model. For this reason, in what follows we use the first PC of Arctic intermodel sTs trend differences as our metric for AA, although we note that sensitivity tests indicate that all results are robust if the analysis is repeated using the conventional AA metric.

Details are in the caption following the image
Area-averaged Arctic (70°–90°N) sTs trends reconstructed using the eigenvectors and eigenvalues for only the first EOF of Arctic intermodel sTs trend differences (x axis—K), plotted against the given model's amplified Arctic Ts trend (y axis—K) calculated by area averaging the Arctic Ts trend and subtracting out the globally averaged Ts trend for the given model. Results based upon CMIP5 simulations forced by the RCP8.5 scenario for the period 2006–2100, and sorted by the magnitude of the reconstructed area-averaged Arctic (70°–90°N) sTs trend values.
Having quantified the magnitude of AA within the models as a function of the first PC of Arctic intermodel sTs trend differences, we now investigate how the magnitude of AA scales with atmospheric energy convergence (AEC) within the Arctic region. To start, we recognize that trends in Arctic AEC, urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0001, must balance any trends in energy lost/gained to the surface (Fsfc) or through the top of the atmosphere (RTOA). Assuming energy storage within the atmospheric column is small, the AEC is given by
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0002(1)
where
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0003(2)
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0004(3)

Here SW represents shortwave fluxes, LW represents longwave fluxes, SH represents sensible heat transfer to the atmosphere, LH represents latent heat transfer to the atmosphere, and up (down) arrows represent upward (downward) fluxes of radiation.

Next we recognize that the difference in the absorbed shortwave radiation at the top of the atmosphere urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0005 and the absorbed shortwave radiation at the surface urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0006 comprises the shortwave absorption by the atmosphere, (SWatm), such that
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0007(4)
In turn we can write Arctic AEC as follows:
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0008(5)
where urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0009 represents only the cycling of terrestrial energy between the Earth's surface and atmosphere:
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0010(6)

Plotting trends in these quantities with respect to a given model's AA value (Figure 4) shows that trends in total AEC decrease with increasing AA, as has been reported elsewhere (e.g., Pithan & Mauritsen, 2014), such that low-AA models tend to be characterized by small to negligible increases in total AEC while high-AA models tend to be characterized by decreases in total AEC. Interestingly, there is very little relation between intermodel differences in AA and trends in SWatm; rather, the predominant balancing term for the decrease in AEC as a function of AA is an increase in urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0011. This increase in surface heat flux to the atmosphere with increasing AA is partially offset by increasing urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0012 as a function of AA.

Details are in the caption following the image
Trends in atmospheric energy convergence (AEC) over the Arctic (70°–90°N), plotted as a function of the given model's reconstructed area-averaged Arctic (70°–90°N) sTs trend, as in Figure 3. Black—total AEC; green—AEC generated by longwave emissions through the top of the atmosphere; blue—AEC generated by net energy transfer between the surface and atmosphere; and red—AEC generated by atmospheric absorption of shortwave radiation. Sign convention such that positive (negative) values represent convergence (divergence) of atmospheric energy generated by cooling (heating) of the atmospheric column, as designated by the arrows on the RHS of the panel—see also text for details.
We now examine the mechanisms accounting for the surface- and TOA-generated AEC trends. To start, we examine what gives rise to trends in longwave emissions to space. First we recognize that urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0013can be equated to an (hypothetical) effective radiating temperature, Teff, via the Stephan-Boltzmann's equation:
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0014(7)
In turn changes in urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0015 can be approximated by
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0016(8)
where urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0017 is the initial effective radiating temperature and ∂Teff is the change in the effective radiating temperature at a given grid. The latter can be decomposed into a contribution from a change in temperature at the initial effective radiating height, urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0018 (first term in right-hand side, RHS, brackets) and a contribution from a change in the effective radiating height itself, ∂zeff, given the (final) environmental lapse rate, urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0019 (second term in RHS brackets). While an in-depth analysis of these influences is not critical to our analysis, we do find that the effective radiating height for all models increases; absent any changes in the temperature profile this change reduces the effective radiating temperature and hence longwave emission to space. However, in all models, changes to the temperature profile—both as a result of a uniform increase in temperatures as well as change in the environmental lapse rate—are sufficiently large to increase the temperature at the new effective radiating level above the original effective radiating temperature, thereby enhancing longwave emission to space and increasing AEC. Further, we note that the increase in TOA-generated AEC as a function of AA is driven by an enhanced warming of the atmospheric column as a function of AA, which in turn is partially offset by reduced transmission of longwave radiation and increased effective radiating heights with increased AA.

More germane to our primary findings are the contributors to the surface-generated AEC trends. In this case, all necessary flux values—including latent and sensible heat transfer along with longwave emissions to and from the surface—are directly available from the models. Plotting these against a given model's AA value (Figure 5) indicates that the decrease in surface-generated AEC as a function of AA results from increasing latent heat flux to the atmosphere with increasing AA, as well as from decreasing (negative) net longwave emissions to the atmosphere with increasing AA. For the latter, it is important to note that in all models the net transfer is positive, that is, from the surface to the atmosphere, and hence generates divergence of energy from the atmospheric column. In low-AA models, the net longwave transfer is reduced because the trend in emissions from the atmosphere to the surface exceeds the trend in emissions from the surface to the atmosphere (not shown); hence, the trend in net longwave emissions necessitates enhanced AEC in the low-AA models. As AA increases, however, the trend in emissions from the surface to the atmosphere comes back into equilibrium with the trend from the atmosphere to the surface (not shown) and the overall trend in net emissions tends toward 0 in high-AA models, similar to what occurs on synoptic time scales (Woods & Caballero, 2016). It is also important to note that intermodel spread in sensible heat transfer over the Arctic is not a function of AA.

Details are in the caption following the image
Trends in surface-generated atmospheric energy convergence (AEC) over the Arctic (70°–90°N), plotted as a function of the given model's reconstructed area-averaged Arctic (70°–90°N) sTs trend, as in Figure 3. Black—total surface-generated AEC; green—AEC generated by the net longwave emission from the surface to the atmosphere; red—AEC generated by turbulent transfer of sensible heat from the surface to the atmosphere; and blue—AEC generated by turbulent transfer of latent heat from the surface to the atmosphere. Sign convention such that positive (negative) values represent convergence (divergence) of atmospheric energy generated by cooling (heating) of the atmospheric column by the various processes, as designated by the arrows on the LHS of the panel—see also text for details.
From the point of view of the surface-generated Arctic AEC trends, then, moisture plays a critical role in its response to AA. It is possible to quantify this role by decomposing Arctic AEC into components associated with convergences of dry static energy (DSE) and moisture:
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0020(9)
Explicit calculation of both terms on the RHS of equation 9 requires vertically integrating the mass-weighted horizontal fluxes of moisture, temperature, and potential energy. However, we recognize that the second term on the RHS—which is related to the net horizontal flux convergence of moisture into and out of the atmospheric column—can be approximated by the net vertical flux divergence of moisture (assuming time rate changes of storage are small). In this case
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0021(10)
where P is the rate of precipitation, E is the rate of evaporation, and Lv is the latent heat of vaporization of water. Using equation 10 moisture-generated AEC can be estimated from model output. In turn the DSE-generated AEC can be calculated as the residual between the total AEC and the moisture-generated AEC. Plotting these three terms as functions of AA (Figure 6a) indicates that in all models, DSE-generated AEC is negative and decreasing with AA, which is consistent with expectations based on a decreasing meridional temperature gradient with increasing AA (Hwang et al., 2011; Kay et al., 2012). More interestingly, it appears that moisture-generated AEC is invariant as a function of AA. By contrast, when the components of Arctic AEC are plotted as functions of global mean Ts (Figure 6b), it is apparent that moisture-generated Arctic AEC is a function of the global temperature change signal, rather than the regional Arctic temperature change signal, suggesting it is driven by changes in atmospheric moisture content originating at lower latitudes. Meanwhile, the total Arctic AEC, which shows a strong relation to AA (cf. Figure 6a), is insensitive to global mean Ts. These results highlight that when considering Arctic AEC, different processes impact different AEC components, and by extension must be considered and analyzed at different scales.
Details are in the caption following the image
(a) Trends in atmospheric energy convergence (AEC) over the Arctic (70°–90°N), plotted as a function of the given model's reconstructed area-averaged Arctic (70°–90°N) sTs trend, as in Figure 3. Black—total AEC; red—AEC generated by convergence of dry static energy (DSE) over the Arctic; blue—AEC generated by convergence of moisture over the Arctic. By construction, the blue and red values sum to the values designated by the black dots. (b) Same as Figure 6a except plotted as a function of the given model's global mean Ts trend.
Returning to the moisture-generated AEC, its invariance as a function of AA appears to be counterintuitive given the relation between total AEC, latent heat transfer (via evaporation), and AA found previously. Taken together, however, these results suggest that Arctic precipitation increases with increasing AA in such a way that precipitation minus evaporation is invariant with AA. To investigate this possibility, we combine the decomposition of Arctic AEC via dry/moist processes (equations 9 and 10) with the decomposition via heating processes (equations 5 and (6)). Setting these equal and recognizing that the latent heat term in equation 6 cancels with the evaporation term in equation 10, we arrive at the following equation:
urn:x-wiley:2169897X:media:jgrd54368:jgrd54368-math-0022(11)

The two terms on the left-hand side (LHS) of the equation represent heating of the atmospheric column via convergence of DSE and latent heat release via precipitation, respectively. The first term on the RHS of the equation represents the total net loss of longwave radiation by the atmosphere—which we will designate as LWatm—while the second and third terms represent heating of the atmosphere by sensible heat transfer and absorption of solar radiation, respectively. Plotting these five terms as a function of AA (Figure 7) reveals an emergent behavior over the Arctic. In particular, the heating of the atmosphere via sensible heat transfer and absorption of solar radiation are constant as a function of AA (as found earlier). In addition, while the emission of longwave radiation to space increases as a function of AA (cf. Figure 4), the net emission to the surface decreases (cf. Figure 5) and hence LWatm is also constant as a function of AA (in contrast to intermodel differences found at the global scale—Pendergrass & Hartmann, 2014). As such, the RHS of equation 11 is constant as a function of AA. By construction, then, the LHS of the equation is constant as a function of AA, which in turn prescribes that the decrease in DSE-generated AEC as a function of AA is balanced predominantly by increasing precipitation as a function of AA.

Details are in the caption following the image
(a) Trends in atmospheric energy convergence (AEC) over the Arctic (70°–90°N) as found in equation 11, plotted as a function of the given model's reconstructed area-averaged Arctic (70°–90°N) sTs trend, as in Figure 3. Black—AEC generated by convergence of dry static energy (DSE) over the Arctic; blue—AEC generated by precipitation over the Arctic; green—AEC generated by net longwave emissions from the atmosphere; red—AEC generated by turbulent transfer of sensible heat from the surface to the atmosphere; and yellow—AEC generated by atmospheric absorption of shortwave radiation.

This emergent behavior can be rendered more explicit by plotting DSE-generated AEC as a function of Arctic precipitation (Figure 8a). This relation—which is one of the strongest of those shown here (r = −0.81)—suggests that Arctic precipitation increases are constrained by the magnitude of AA through its influence on DSE-generated AEC. Returning to Figure 7, we can understand this constraint by noting that for all models the heating of the atmosphere by sensible heat transfer and absorption of solar radiation, in addition to being invariant as a function of AA, are both relatively weak. Further, for all models the total net loss of longwave radiation by the atmosphere is positive definite such that it cannot balance the anomalous DSE-generated cooling. As such Arctic precipitation increases are not arbitrary but are in fact constrained by the necessity for column (latent) heating to balance the anomalous DSE-generated cooling that accompanies enhanced AA. Further confirmation of this emergent relation can be found by plotting the DSE-generated AEC against precipitation-generated AEC at all times for each of the 18 individual models (Figure 8b). While initial values of both quantities differ across the models, a significant linear relation between the two is found in all models, regardless of AA strength.

Details are in the caption following the image
(a) Trends in atmospheric energy convergence (AEC) over the Arctic (70°–90°N) generated by precipitation over the Arctic (y axis) plotted as a function AEC generated by convergence of dry static energy (DSE) over the Arctic (x axis). (b) The 20 year mean value of precipitation-generated AEC over the Arctic plotted as a function of DSE-generated AEC across the duration of each model simulation (2005–2099). Each line represents a separate model; models are color coded by the magnitude of the reconstructed area-averaged Arctic (70°–90°N) sTs trend values, as in Figure 3.

To further investigate this emergent relation, we next examine the time evolution of the weighted composite-mean Arctic AEC trends for low- and high-AA models, as determined by the model weightings from the leading mode of Arctic sTs trend variability. While the low-AA models manifest quasi-linear evolution of AEC trends across the full simulation period, the high-AA models—which initially track the low-AA evolution closely through the first half of the simulation—depart from this linear behavior with respect to both DSE-generated and precipitation-generated AEC, in concert with the nonlinear evolution in AA itself (not shown). Performing a similar analysis of the surface energy convergence terms indicates that this nonlinear time evolution is also found in the latent heating (equivalently evaporation) and the absorbed shortwave energy at the surface (not shown). To compare the time evolution of the high- and low-AA models, we difference the composite-mean values of DSE-generated and precipitation-generated AEC, absorbed shortwave energy at the surface, and the common term related to latent heat transfer to the atmosphere, along with the total Arctic AEC, and plot these as functions of time (Figure 9a). Doing so highlights that the initial (nonlinear) deviation in the evolution of the high-AA models vis-à-vis the low-AA models is associated with absorbed shortwave energy at the surface, followed approximately a decade later by simultaneous deviations of the four AEC terms. To better discern the relation between these four terms and the evolution of AA itself, the composite-mean differences of the energy budget terms are plotted against the composite-mean differences in AA over time (Figure 9b). We find that the difference in the composite-mean evolution of AA between the high- and low-AA models scales linearly with differences in the absorbed shortwave energy at the surface and hence is coincident with it. By contrast, differences in the AEC-related evolutions are delayed relative to the differences in AA itself (as evidenced by the near-zero AEC-related difference values at low but nonzero AA difference values), suggesting that enhanced precipitation-generated AEC is a response to, not a cause of, the enhanced AA in high-AA models.

Details are in the caption following the image
(a) Difference in composite-mean trends in reconstructed area-averaged Arctic (70°–90°N) sTs (thick, amber lines) and energy convergence (thin lines) plotted as a function of time. Black—total atmospheric energy convergence (AEC); yellow—surface energy convergence (SEC) generated by absorption of shortwave radiation at the surface; red—AEC generated by turbulent transfer of latent heat from the surface to the atmosphere; blue—AEC generated by precipitation over the Arctic; and black—AEC generated by convergence of dry static energy (DSE) over the Arctic. For all AEC-related terms, sign convention such that positive (negative) values represent convergence (divergence) of atmospheric energy generated by cooling (heating) of the atmospheric column; for absorbed shortwave, sign convention such that positive (negative) values represent convergence (divergence) of surface energy. (b) Same as Figure 1a except area-averaged Arctic (70°–90°N) energy convergence composite-mean trend difference (y axis) plotted as a function of reconstructed area-averaged Arctic (70°–90°N) sTs composite-mean trend difference (x axis).

How, then, should we interpret this full set of results? Considering only the DSE-generated and total AEC terms would suggest the conventional interpretation that as AA increases, DSE-generated AEC is reduced as a direct consequence of the reduced meridional gradient in temperature (Figure 6a). However, the added constraint that both precipitation-generated and evaporation-generated AEC scale with AA (Figures 5 and 7) suggests another possibility. Namely, we recognize that absent any other AA-related atmospheric heating processes, the first-order balance to the anomalous DSE-generated cooling must be latent heating associated with enhanced precipitation. Further, we recognize that as AA increases—most likely through reduction of sea ice leading to enhanced absorption of shortwave radiation at the surface—evaporation increases, increasing the moist static energy of the column. Results here suggest local surface-generated MSE leading to ascent culminates in enhanced precipitation and subsequent export of anomalous DSE from the region aloft. It is important to recognize that these terms are all deviations from the climatological behavior, and as such may not represent actual vertical ascent but a reduction in the strength of the descending branch of the polar cell, and by extension a reduction in the export of (low) DSE from the region. At the same time, these results implicate the role of moist processes (and enhanced precipitation) in mediating DSE-generated AEC associated with enhanced AA, a fact not fully appreciated until now.

The final question to be addressed here is how this enhanced precipitation (and export of DSE) is manifested in high-AA models, vis-à-vis low-AA models. As discussed elsewhere (Bengtsson et al., 2011; Bintanja & Selten, 2014; Kopec et al., 2016; Kug et al., 2010; Liu & Barnes, 2015; Woods et al., 2013) one option is that the excess precipitation is all generated “locally” by evaporation-supplied moisture. Alternatively, while evaporation may perturb the precipitation-producing processes (e.g., by reducing the static stability of the atmosphere and making it more conducive for generating precipitation), some of the moisture that precipitates out may arrive from outside the Arctic itself. While this question will not be answered conclusively here, composite-mean maps of precipitation and sTs trends for high- and low-AA models (Figure 10) suggest that Arctic precipitation trends for both low and high-AA models are coherent with extrapolar trends in the subpolar North Atlantic, eastern North America, and, particularly in high-AA models, the Asian continent. Differencing these fields highlights the strengthened connection between amplified Arctic precipitation and precipitation over the Asian continent, at the expense of its connection with precipitation over the North Atlantic. Further, although less robust, there is a geographic relation between enhanced extrapolar warming in the high-AA models with reduced precipitation, again most prominently over the North Atlantic but also apparent over the North Pacific as the well midlatitudes of North America and central Eurasia. In these regions, then, it may be that the local export of DSE enhances temperatures, thereby amplifying sTs trends but also stabilizing the atmosphere and hence reducing precipitation in these regions.

Details are in the caption following the image
(a) Shading: Weighted composite-mean precipitation trends for positive model weightings of the first PC of Arctic intermodel sTs trend differences (mm/day). Shading interval given by top color bar at right. Contours: Weighted composite-mean sTs trends for positive model weightings of the first PC of Arctic intermodel sTs trend differences (K). Contour interval as labeled. Black circle indicates Arctic region analyzed throughout. (b) Same as Figure 10a except for negative model weightings of the first PC of Arctic intermodel sTs trend differences. (c) Difference between positive and negative composite maps as shown in Figures 10a and 10b. Shading interval given by bottom color bar at right. Contour interval as labeled. (d) Precipitation (mm/day) and sTs (K) trends regressed on the model weightings of the first PC of Arctic intermodel sTs trend differences, multiplied by 2.

4 Discussion

In the preceding section, we use projections of future changes of the Arctic climate derived from fully coupled Earth system models to reveal an emergent behavior in which enhanced AA and its associated reduction in dry static energy convergence is balanced to first order by latent heating via enhanced precipitation. Here we attempt to determine whether the emergent behavior revealed through the CMIP5 analysis is consistent with the observed behavior over the historical record. The largest impediment to such a model evaluation is the dearth of robust, extensive, continuous, and accurate observations in this region, particularly with regard to the water cycle (e.g., ACIA, 2005; Hegerl et al., 2015; Vihma et al., 2016; Walsh et al., 2011). Further, reanalysis-based estimates show large discrepancies in both the energy and moisture budgets in this region (DuFour et al., 2016; Porter et al., 2010; Serreze et al., 2007). For this reason, we confine ourselves to the relatively direct, and simple, comparison of Arctic precipitation trends with AA of the overall global mean temperature trend. For observationally based estimates of precipitation, we use both the CPC Merged Analysis of Precipitation (CMAP) (Xie & Arkin, 1997) and Global Precipitation Climatology Project (GPCP) Version 2.3 combined precipitation (Adler et al., 2003) data, which are available at monthly time resolution from 1979 to 2016. The values are regridded from their native resolution to the same spatial resolution as the CMIP5 data. Grid point trends are calculated over the 36 year period 1979–2016 using the same method as described above. These trend values are then area averaged over the Arctic (70°–90°N).

For observationally based estimates of temperature, we find that commonly used gauge- and satellite-based records (including those found in HadCRUT4 (Morice et al., 2012), the NASA Goddard Institute for Space Studies (Hansen et al., 2010), and NOAA Global Surface Temperature V4.0.1 (Smith et al., 2008)) still have substantial missing data in the Arctic region. For that reason here we rely on observationally constrained reanalysis-based estimates, which are generally well representative of the observed state and show good agreement across data sets (Lindsay et al., 2014; Simmons & Poli, 2015). For our purposes we analyze data from the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 (Kanamitsu et al., 2002) and ERA-Interim (Dee et al., 2011). As with the precipitation data, the near-surface (2 m) temperature values are regridded from their native resolution to the same spatial resolution as the CMIP5 data and grid point trends are calculated over the 36 year period 1979–2016 using the same method as described above. Since we are mainly interested in the magnitude of AA over the course of the observational record and not necessarily how its structure maps onto the intermodel difference pattern, we additionally revert to a conventional measure of AA, namely, the difference in the area averaged Ts trend values from 70° to 90°N minus the global mean trend values.

For comparison with the observationally based estimates of Arctic precipitation and amplification over the same time period, we concatentate the CMIP5 Historical simulation from a given model, which ends in 2005, with its continuation along the RCP8.5 trajectory, which commences in 2006. We then subselect data from the time period 1979–2016 and calculate the trends in Arctic precipitation and AA, as described above.

Two important results are obtained from this analysis (Figure 11). First, while there is large uncertainty in observed precipitation over this region (as documented elsewhere (ACIA, 2005; Vihma et al., 2016; Walsh et al., 2011) and as evident by the large difference in estimated trends from the CMAP and GPCP data), the emergent behavior revealed through the analysis of the CMIP5 data is qualitatively consistent with the observed behavior over the historical record. In particular, based upon the reanalysis data, the historical trajectory of observed AA is at the high end of the model projections for this time period. Correspondingly, the historical trajectory of observed Arctic precipitation spans the expected value derived from the CMIP5 results (i.e., the observations are not biased high or low with respect to the model estimate). Further, we find that the sensitivity of Arctic precipitation trends to the strength of AA within a given model (as represented by a least squares linear regression) during the historical period (1.24 W/m2/K) is nearly identical to that found over the course of the 21st Century under the RCP8.5 forcing (1.26 W/m2/K). We argue that the consistency of these two values across very different boundary forcing conditions, along with the fact that the observed record shows consistent changes to those in the models, is further evidence of a fundamental energetic constraint on Arctic precipitation as a function of AA.

Details are in the caption following the image
Trends in atmospheric energy convergence (AEC) over the Arctic (70°–90°) generated by precipitation (y axis—W/m2) plotted as a function of the amplified Arctic Ts trend (x axis—K) calculated by area averaging the Arctic Ts trend and subtracting out the globally averaged Ts trend. Results based upon CMIP5 simulations forced by the RCP8.5 scenario for the period 2006–2100 (red dots); CMIP5 simulations forced by the historical and RCP8.5 scenarios for the period 1979–2016 (blue dots); historical estimates of precipitation from the Global Precipitation Climatology Project (GPCP—black circles) Version 2.3 combined precipitation data set and the CPC Merged Analysis of Precipitation (CMAP—black diamonds) data set for the period 1979–2016; and historical estimates of near-surface (2 m) air temperature from the NCEP-DOE Reanalysis 2 (R2—black filled markers) data set and the ERA-Interim (ERA—black open markers) data set for the period 1979–2016.

5 Summary

In this paper we used the fully coupled atmosphere/ocean climate models of CMIP5 forced by similar trajectories of human-induced emissions of carbon dioxide and other greenhouse gases (RCP8.5) to quantify intermodel differences in the magnitude and structure of amplified warming projected to occur in the polar regions, with a specific focus on the Arctic. In fact, we explicitly demonstrate that some of the largest intermodel differences in surface warming, after accounting for differences in global mean temperature increases resulting from differing model climate sensitivities, occur over this region. Within the suite of CMIP5 models, enhanced Arctic amplification (AA) of the global temperature increase is accompanied by reduced atmospheric energy convergence (AEC).

Disaggregating the heating and cooling processes associated with AEC, we find relatively little relation between a model's AA strength and changes in atmospheric absorption of solar radiation within the model. Further, we find that models with enhanced AA tend to experience greater radiative cooling to space, which subsequently would need to be balanced by enhanced AEC rather than reduced AEC. However, the enhanced radiative loss to space is balanced by reduced (net) longwave radiative loss to the surface such that the change in net longwave radiative cooling (of the atmosphere) tends to be invariant with AA (which stands in contrast to intermodel differences at the global scale—Pendergrass & Hartmann, 2014).

Instead, reduced AEC within high-AA models is tied to increased surface turbulent (sensible and latent) heat fluxes. For all models, the change in sensible heat flux is small and effectively invariant with AA. Instead, reduced AEC in high-AA models is balanced principally by enhanced latent fluxes resulting from enhanced evaporation. However, the overall moisture flux convergence, and by extension atmospheric latent heat convergence, into the Arctic is not a function of AA, although we note that it is a linearly increasing function of overall global mean temperature change. This result has two important ramifications: (1) reduced AEC within high-AA models is primarily achieved through reduced dry static energy (DSE) fluxes into the Arctic and (2) enhanced Arctic evaporation within high-AA models is balanced by enhanced Arctic precipitation.

Together, these characteristics of high-AA models suggest an emergent relationship between the amplification of Arctic temperature increases (i.e., AA) within a given model and the amplification of Arctic precipitation increases in that model. Namely, within high-AA models, reduced AEC associated with reduced DSE convergence is balanced to first order by latent heat release via enhanced precipitation, that is, amplification of Arctic warming leads to amplification of Arctic precipitation. At the same time, the magnitude of these precipitation increases in the Arctic is constrained by the strength of AA via its influence on DSE fluxes into the Arctic.

More generally, the emergent behavior revealed through the analysis of the CMIP5 data—which is present across various boundary forcing conditions and is consistent with the observed behavior over the historical record—suggests underlying linkages between the energetics and hydrometeorology of the Arctic that may be relevant even outside the context of amplified warming of the Arctic in response to anthropogenic forcing. For instance, we show that within a given model changes in 20 year annual mean averages of DSE-generated AEC over time are linearly related to changes in Arctic precipitation. As such it may be that similar balances hold on interannual time scales as well (as found in other regions of the globe—e.g., Su & Neelin, 2003), although results are expected to be sensitive to how annual mean values are calculated with respect to the seasonal cycle. Further, we note that while this analysis does not necessarily clarify the mechanistic processes linking enhanced Arctic precipitation to enhanced DSE-generated AEC, nor to enhanced evaporation within high-AA models, recognition of the linkages between AEC and precipitation over the Arctic, as a function of AA, may help constrain various hypothesized processes. Indeed, multiple dynamic and thermodynamic processes could generate enhanced precipitation in response to enhanced AA (e.g., Abbot et al., 2009; Woods et al., 2013). In addition enhanced precipitation could draw from multiple local and/or remote sources of moisture (Bengtsson et al., 2011; Bintanja & Selten, 2014; Kopec et al., 2016; Kug et al., 2010; Singh et al., 2016). We argue that consistency with the emergent behavior of Arctic precipitation in response to enhanced Arctic warming as identified here should be a key criteria for the evaluation of these hypotheses.

Acknowledgments

B. T. A. acknowledges support of Department of Energy grant DE-SC0004975. We acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. To obtain model simulations of historical and projected temperatures from the Coupled Model Intercomparison Project 5 (CMIP5) multimodel ensemble used in this study, please see http://pcmdi9.llnl.gov/esgf-web-fe/. CMAP and GPCP Precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. NCEP_Reanalysis 2 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/