Mechanisms of Regional Precipitation Change from Anthropogenic Forcing
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1 Mechanisms of Regional Precipitation Change from Anthropogenic Forcing Jie He Rosenstiel School of Marine and Atmospheric Science University of Miami
2 Challenges in regional precipita0on simula0on GPCC v5 JJA Rainfall (mm/d) CMIP5 JJA Rainfall (mm/d) Low Model Resolution Courtesy Roque V. Cespedes (UM)
3 Can we use High Resolu0on 0me- slice Experiments? High resolution atmosphere-only models forced with projected changes in SST from CGCMs 1. Is two- way coupling important for regional climate change? 2. Is details of SST change important for regional precipitaaon change? 3. Are we gedng realisac regional climate change from CGCMs? 4. What are some pracacal ways forward? Fast & slow precipitaaon responses in the subtropics and extratropics.
4 Impact of Two- way Coupling Introduc0on Method Results Two-way Coupling is important for natural climate variability. Lack of coupling leads to inconsistency b/w atmos and ocean.
5 Impact of Two- way Coupling Introduc0on Method Results The importance of two-way coupling for natural climate variability is well documented. (e.g., Barsugli and Battisti 1998; Wang et al. 2005; Wu et al. 2006) coupled VS uncoupled What about anthropogenic climate change? (Wu et al. 2006)
6 Impact of Two- way Coupling IntroducAon Method Results Compare coupled and uncoupled simulations that have the same atmospheric model and SST & sea ice. Model: CESM Resolution: approximately 2 o for atmosphere & land and 1 o for ocean Simulations: CGCM, 1pctCO2 AGCM, 1pctCO2 (SST and sea ice from CGCM 1pctCO2) CGCM, pre-industrial AGCM, pre-industrial (SST and sea ice from CGCM pre-industrial) Run time: 150 years Climate change: 10-year epoch difference
7 Impact of Two- way Coupling IntroducAon Method Results Error due to lack of two-way coupling is independent of external forcing.
8 Impact of Two- way Coupling IntroducAon Method Results
9 Impact of Two- way Coupling IntroducAon Method Results
10 Impact of Two- way Coupling IntroducAon Method Results dta dt dsst dt = λ A (SST Ta) + F A + N A = λ O (Ta SST ) + F O Ta: air temperature SST: sea surface temperature λ: air-sea interaction coefficient λ A = s -1 λ O = s -1 F: radiative forcing and damping F A =-1.62 W/m 2 F O =2.10 W/m 2 N A : Stochastic forcing (white noise) std(n A )=0.2K/6days timestep=6days
11 1. Is two- way coupling important for regional climate change? No* 2. Is details of SST change important for regional precipita0on change? 3. Are we geong realis0c regional climate change from CGCMs? 4. What are some prac0cal ways forward?
12 PaRern of SST Change Over ocean: warmer- get- werer (e.g., Xie et al. 2010; Ma and Xie 2013; Chadwick et al. 2013; Kent et al. 2015) Relative SST change precip change Ma and Xie (2013) Is the pattern of ΔSST important for precipitation change over land? Introduc0on Method Results
13 PaRern of SST Change IntroducAon Method Results Model Output: CMIP5 (9 models) Experiments: AMIP control (1979~2008 obs SST) Uniform Warming (+4K) Structured warming (ΔSST at 4xCO 2 ) Changes are normalized by each model s global mean TS change.
14 PaRern of SST Change IntroducAon Method Results Land precipita0on is insensi0ve to the parern of SST change.
15 PaRern of SST Change Tropical ΔSST can impact land remotely through Rossby Wave PropagaAon (e.g., Sardeshmukh and Hoskins 1988; Ting and Sardeshmukh 1993; Schneider et al. 2003) Rossby Wave Source (RWS) ς t +V ψ ς = ς D V χ ς RWS' = ( ς D V χ ς )' ς Absolute voracity V ψ D V χ RotaAonal wind Divergence Divergent wind RWS' = ς ' D ς D' ς ' D' V χ ' ς V χ ς ' V χ ' ς ' RWS' = ς D' Absolute VorAcity Changes in upper- level divergence IntroducAon Method Results
16 PaRern of SST Change tropical ΔSST > land Rossby Wave PropagaAon Absolute VorAcity RWS' = ς D' Changes in upper- level divergence Very little Rossby Waves are generated by the pattern of ΔSST. IntroducAon Method Results
17 PaRern of SST Change We can simulate land climate change using AGCM forced with only increased CO 2 and a uniform warming. (results from CESM) corr=0.86 (pr), 0.95 (TS), 0.85 (SLP) IntroducAon Method Results
18 1. Is two- way coupling important for regional precipita0on change? No* 2. Is details of SST change important for regional precipita0on change? Not for land* 3. Are we geong realis0c regional climate change from CGCMs? 4. What are some prac0cal ways forward?
19 Climatological Biases Introduc0on Method Results GPCC v5 JJA Rainfall CMIP5 JJA Rainfall How could we get realisac projecaons if we could not even simulate the climatology? Dependence of precipitaaon change on climatology wet- get- werer (Held and Soden 2006) Climatological P- E Change in P- E
20 Climatological Biases Introduc0on Method Results Cross Model CorrelaAon of PrecipitaAon Climatology vs PrecipitaAon Change (CGCM 1pctCO2) Climatological biases affect projec0on.
21 Climatological Biases Introduc0on Method Results Biases in climatological SST from CMIP5 CGCMs Observa0on: Hadley- NOAA/OI ( ) CGCMs: *Historical ( ) 1pctCO2 ( )
22 Climatological Biases IntroducAon Method Results Model: CESM ResoluAon: 2 o for the atmosphere 1. AGCM simula0ons with SST climatologies from observa0on and CGCMs. ObsSST AGCM VS Coupled CESM (1pctCO2) modelsst AGCM (CanESM2, CNRM- CM5, GISS- E2- H, HadGEM2- ES, MRI- CGCM3) 2. AGCM simula0ons with pa#erns of SST change from individual CGCMs. Uniform AGCM VS Coupled CESM (1pctCO2) modelparern AGCM (CanESM2, CNRM- CM5, GISS- E2- H, HadGEM2- ES, MRI- CGCM3) Same ΔSST, Different SST climatology. Same SST climatology, Different ΔSST.
23 Climatological Biases IntroducAon Method Results Errors due to biases in climatological SST.
24 Climatological Biases IntroducAon Method Results SST biases VS parern of ΔSST (land) parern important SST biases important SST biases have greater impact than a total removal of parern of ΔSST (below the diagonal).
25 1. Is two- way coupling important for regional precipita0on change? No* 2. Is details of SST change important for regional precipita0on change? Not for land* 3. Are we geong realis0c regional climate change from CGCMs? Climatological biases* 4. What are some prac0cal ways forward?
26 High- resolu0on AGCM? for projecang land climate change Pros: ComputaAonally efficient Unbiased SST climatology à best starang point (precip climatology) Con: Can t simulate SST changes directly But.. the pa*ern of ΔSST is not important; ΔSST hasn t changed much from CMIP3 to CMIP5. KnuD and Sedláček (2013)
27 1. High- resolu0on AGCM with observed SST and ensemble mean ΔSST. Similar ideas for seasonal predicaons (e.g., Jia et al. 2015). AGCMs work be*er without coupling? 2. Flux adjustments? Coupled GCM (50 km) Coupled GCM (50 km) with Flux Adjustments
28 1. Is two- way coupling important for regional precipita0on change? No* 2. Is details of SST change important for regional precipita0on change? Not for land* 3. Are we geong realis0c regional climate change from CGCMs? Climatological biases* 4. What are some prac0cal ways forward? HR AGCM? Flux adjustments?
29 Physical Mechanisms of Precipita0on Changes in the Subtropics and Extratropics
30 What drives subtropical and extratropical precipita0on changes? Increasing CO 2 Atmosphere- land model Land- sea warming contrast SST warming Mean SST warming Pa#ern of SST change Fast responses VS Slow responses
31 What do we already know about subtropical and extratropical precipita0on changes? Wet- get- werer & dry- get- dryer Climatological P- E Change in P- E Intensifica0on of moisture transport (Held & Soden 2006)
32 So according to previous studies Subtropical precipita0on decline Extratropical precipita0on increase Increase in Moisture Global mean SST warming
33 Actually, subtropical precipitaaon declines are independent of the mean surface warming Experiments: Fully coupled: 1pctCO2 (yr121~140 yr1~21) CO 2 only: AMIP_CO2 (4xCO 2 ) Mean Warming only: AMIP_mean (+4K) Structured warming: AMIP_future Pattern only: AMIP_pattern = AMIP_future AMIP_mean
34 CO 2 radiaave forcing Land- sea warming contrast PaRern of SST change Mean SST warming Fast response Slow response Abrupt4xCO2
35 Subtropical precipitaaon decline is a fast response. Extratropical precipitaaon increase is a slow response.
36 Experiments: Fully coupled: 1pctCO2 (yr121~140 yr1~21) CO 2 only: AMIP_CO2 (4xCO 2 ) Mean Warming only: AMIP_mean (+4K) Structured warming: AMIP_future Pattern only: AMIP_pattern = AMIP_future AMIP_mean AMIP_CO2: Direct radiaave forcing of CO 2 Land- sea warming contrast aqua_co2: radiaave forcing only
37 δp in AMIP_CO2 StabilizaJon or Land- sea? Thermodynamic change Dynamic change EvaporaAon change Eddy transport
38 The stabilizing effect of CO 2 does not weaken convecaon in the subtropics.
39
40 References Barsugli, J., and D. S. BaDsA, 1998: The basic effects of atmosphere- ocean thermal coupling on midlaatude variability. J. Atmos. Sci., 55, 477. Chadwick, R., I. Boutle, and G. MarAn, 2013: SpaAal parerns of precipitaaon change in CMIP5: why the rich do not get richer in the tropics. J. Clim., 26, Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Clim., 19, Jia, L. and Coauthors, 2015: Improved Seasonal PredicAon of Temperature and PrecipitaAon over Land in a High- ResoluAon GFDL Climate Model. J. Clim., 28, Kent, C., R. Chadwick, and D. P. Rowell, 2015: Understanding uncertainaes in future projecaons of seasonal tropical precipitaaon. J. Climate, accepted. KnuD, R., and J. Sedláček, 2013: Robustness and uncertainaes in the new CMIP5 climate model projecaons. Nature Clim. Change, 3, Ma, J., and S. Xie, 2013: Regional PaRerns of Sea Surface Temperature Change: A Source of Uncertainty in Future ProjecAons of PrecipitaAon and Atmospheric CirculaAon. J. Clim., 26, Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generaaon of global rotaaonal flow by steady idealized tropical divergence, J. Atmos. Sci., 45, Schneider, E. K., L. Bengtsson, and Z. Hu 2003: Forcing of Northern Hemisphere climate trends, J. Atmos. Sci., 60( ), Ting, M., and P. D. Sardeshmukh 1993: Factors determining the extratropical response to equatorial diabaac heaang anomalies, J. Atmos. Sci., 50, Vecchi, G. A. and Coauthors, 2014: On the Seasonal ForecasAng of Regional Tropical Cyclone AcAvity. J. Clim., 27, Wang, B., Ding, Q., Fu, X., Kang, In- Sik, Jin, K, J. Shukla and Doblas- Reyes, F., 2005: Fundamental challenge in simulaaon and predicaon of summer monsoon rainfall. Geophys. Res. LeR., 32 Wu, R., B. P. Kirtman, and K. Pegion, 2006: Local air- sea relaaonship in observaaons and model simulaaons. J. Clim., 19, Xie, S., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. WiRenberg, 2010: Global Warming PaRern FormaAon: Sea Surface Temperature and Rainfall. J. Clim., 23, Thank you J
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