On assessing temporal variability and trends of coupled arctic energy budgets Michael Mayer Leo Haimberger
Motivation and outline Arctic climate system is subject to rapid changes and large interannual variability Earlier work focused on the mean atmospheric energy budget Investigate the coupled energy budget of atmosphere and ocean, including sea ice à Can we quantify trends and variability in energy flows with stateof-the-art datasets? For now only 2000-2014! Arctic sea ice fraction anomaly
Mean energy budget of the polar cap Main balance between horizontal energy transport and radiative energy loss at TOA Observed ocean warming and ice melt rates are small residual of large fluxes à Sum of the fluxes across 70N and TOA should yield mean energy storage in the polar cap! Rad TOA 70N F A (F I ) IHCT<0 F S OHCT>0 AET~0 F S IHCT<0 F IB 70N F A F O FF O O
Atmosphere What we have: Rad TOA : satellite data (CERES)! " F A : mass-corrected from atmospheric reanalyses F S : directly from model-forecasts (biased) or indirectly from other atmospheric fields (accumulated errors) Ocean! " F O : directly computed from ORAS4 (soon: ARCGATE) OHCT: from ocean reanalyses with very different data assimilation approaches Sea ice! " F A = Rad TOA # AET # F S! " F O = (1-f i )F S +f i F IB -OHCT! " F I = f i F S -f i F IB -IHCT Sea ice mass from coupled ocean-ice reanalyses (ORAP5) or ice reanalyses (PIOMAS) Sea ice extent to understand changes in fluxes
The state of the coupled Arctic energy budget (70-90N) Add up atmosphere, ocean, ice equations and integrate over polar cap { Rad TOA }+ F A (70N) + F O (70N) + F I (70N) = OHCT { }+{ IHCT}+ { AET}+ + imbalance 2000-2014 averages: Net heat flux into polar cap [Wm - 2 ] Heat storage [Wm - 2 ] Rad TOA (CERES) - 116.1±0.4 F A across 70N 95.3 (92.3,98.7) atmosphere 0.1±0.1 F O across 70N 18.5±0.4 Ice melt 0.3±0.3 F I across 70N 3 (Serreze et al 2007) Ocean heat content 0.7±0.5 Sum of fluxes 0.3±0.9 Storage sum 1.1±0.6
Trends in the coupled budget of the Arctic (70-90N) Add up atmosphere, ocean, ice equations and integrate over polar cap { Rad TOA }+ F A (70N) + F O (70N) + F I (70N) = OHCT { }+{ IHCT}+ { AET}+ + imbalance 2000-2014 averages: Net heat flux into polar cap [Wm - 2 ] Heat storage [Wm - 2 ] Rad TOA (CERES) - 116.1±0.4 F A across 70N 95.3 (92.3,98.7) atmosphere 0.1±0.1 F O across 70N 18.5±0.4 Ice melt 0.3±0.3 F I across 70N 3 (Serreze et al 2007) Ocean heat content 0.7±0.5 Sum of fluxes 0.3±0.9 Storage sum 1.1±0.6 Sampling uncertainty is small, but structural uncertainties are likely large!
Variability: Budget time series for 70N-90N Clear relationship between surface energy flux and sea ice extent Atmospheric energy transport exhibits strong variability, especially associated with low ice extents in 2011, 2012
Example Extreme case of very low sea ice extent in fall 2007 Surface energy fluxes and Rad TOA in JJA 2007 clearly resemble the pattern of sea ice anomalies in Sep 2007 Sea ice anomalies Sep 2007 Surface flux anomalies JJA 2007 Rad TOA anomalies JJA 2007 (fraction) (Wm -2 ) (Wm -2 ) (data: ERA-I) (data: CERES+ERA-I) (data: CERES)
Radiation at TOA and sea ice extent Summer (JJA) Rad TOA clearly has strong impact on September sea ice extent Reanalysis has difficulties to show this, probably due to cloud biases Area averages 70N-90N Area averages 70N-90N R=-0.44 Rad TOA from ERA-I in JJA (Wm -2 ) R=-0.87 Rad TOA from CERES in JJA (Wm-2)
Seasonal trends: atmosphere Positive summer trend of FS driven by RadTOA à ice-albedo feedback Negative autumn trend of FS driven by atmospheric energy convergence associated with decreased baroclinicity! " FA RadTOA Surface flux [K 1000km-1decade-1] (CERES) (CERES+reanalysis ensemble) (reanalysis ensemble) (ERA-I) Thickness gradient
Seasonal trends: atmosphere Positive summer trend of FS driven by RadTOA à ice-albedo feedback Negative autumn trend of FS driven by atmospheric energy convergence associated with decreased baroclinicity! " FA RadTOA Surface flux [K 1000km-1decade-1] (CERES) (CERES+reanalysis ensemble) (reanalysis ensemble) (ERA-I) Thickness gradient
Seasonal trends: ocean and ice Changes in energy used for melting/freezing consistent with FS changes Very noisy OHC trend picture Surface flux Melting/freezing (CERES+reanalysis ensemble) (ORAP5) OHC tendency (ORAP5)
Upper ocean heat content trends 2000-2014 Different estimates for end-of-melt-season OHC trends agree qualitatively Lack of spring trends in HEN4 demonstrates importance of dynamical ocean reanalysis to carry information forward in time Seasonal OHC300 trends (70-90N) Yearly cycle of observations in HEN4
Summary Largely independent data yields reasonable closure of coupled arctic energy budget Results show the expected tight relation between sea ice and energy budget Large seasonal trends in all budget terms over 2000-2014 Outlook Proposal for FWF project submitted, under review Thoroughly assess uncertainty Get better ocean estimates from in-situ data (ARCGATE) and coupled reanalyses Extend study period backward in time Try to quantify contributions from natural variability to observed seasonal trends
1 g Atmospheric energy budget Energy conservation in the vertically integrated atmosphere: p s $! "(c p T + # + k + Lq)v 2 dp = (ASR + OLR) % & &t 0 F A 1 g $ (c p T + # S + k + Lq)dp % (LH +SH + Rad S ) DIVFA Rad TOA AET F S p s 0 Compute DIVFA and AET from analyzed reanalysis fields: ERA- Interim, MERRA, JRA55 Difficulty with computation of DIVFA: mass budget in reanalysis Rad TOA and F S from reanalysis or independent satellite data: CERES (soon: ERBE before 2000)
Oceanic energy budget Energy conservation in the vertically integrated ocean: bottom '! 0 c water $ " #(%v 2 )dz = LH +SH + Rad S &! 0 c water 't 0 DIVFO F O F S! DIVFO = F S " OHCT bottom $ 0 OHCT OHC Direct evaluation of DIVFO requires horizontal fluxes in the oceans (ocean currents): available from ORAS4 Soon: ocean heat transport from in-situ data (ARCGATE array) and coupled reanalysis from ECMWF (CERA) Indirect evaluation of DIVFO requires F S and OHC: F S indirectly from atmospheric reanalyses and satellite data OHC from oceanic reanalyses: ORAS4, ORAP5, Hadley EN4 %dz