Horyuji PAGODA

Horyuji PAGODA h#p://ncas-­‐climate.nerc.ac.uk/pagoda HydrOlogical cYcle Understanding vIa Process-­‐bAsed GlObal Detec=on, A?ribu=on and predic=on P.L. Vidale and 1) the PAGODA team 2) JWRCP “High-­‐ResoluHon Climate Modelling” team A partnership in weather and climate research
Scope: PAGODA is a collaboraHve project focussing on the global dimensions of changes in the water cycle in the atmosphere, land, and oceans. The scienHfic scope prioriHses themes 2,1,3,4 in the AO, adopHng a focus on climate processes to extend our understanding of the causes of water source/sink uncertainty and to provide new, robust predicHons at sub-­‐conHnental scales. Hōryū-­‐ji (法隆寺 Temple of the Flourishing Law), one of the two oldest wooden buildings in the world, 532 AD. The image cannot be
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Aims of PAGODA Our overarching aim is to make more reliable projec9ons of the changing water cycle on a process-­‐based detec9on, a>ribu9on and decadal and global-­‐to-­‐regional scales, through predic9on. Specific aims are: A.  Assess and quanHfy how the water cycle is changing on global-­‐to-­‐regional scales and decadal Hmescales. B.  Evaluate, against observaHons at a process level, the representaHon of variability and changes in the water cycle in exisHng climate models. C.  IdenHfy the processes in the atmosphere, land and oceans that are responsible for any divergence with observaHons and amongst exisHng climate models in hindcast and forecast changes in the water cycle. D.  Provide robust guidance to users about possible future changes in the water cycle, based on the evaluated reliability of processes in climate models. PAGODA seeks to exploit important new opportuniHes for research progress, including new observaHonal data sets (e.g. ocean salinity reanalysis, TRMM and SSMIS satellite products, long precipitaHon records), new models (HadGEM3 & new capabiliHes for high resoluHon simulaHons), the new CMIP5 model inter-­‐comparison project (h#p://cmip-­‐pcmdi.llnl.gov/cmip5/index.html) and to develop new methodologies for process-­‐based detecHon, a#ribuHon and predicHon. PAGODA targets directly the first two goals of the Changing Water Cycle programme (“… integrated, quanHtaHve understanding of the changes … and … improve predicHons for the next few decades …”), and will make major contribuHons to all four research themes. Its aims are also closely aligned with the highest strategic prioriHes of major internaHonal programme (e.g. WCRP, CLIVAR, GEWEX). Work packages The research programme consists of five work packages (WP): WP1: The Changing Water Cycle in GCMs WP2: Processes controlling changes in the global water cycle WP3: Observed changes in the global water cycle WP4: DetecHon and A#ribuHon WP5: Robust user-­‐relevant a#ribuHon assessments and projecHons of future changes in the water cycle, including extremes Figure 2: Work packages and their linkages"
WP2: Processes controlling changes in the global water cycle Lead: P.L. Vidale (Reading) The overarching goal of WP2 is to idenHfy and understand the major processes that govern changes in the water cycle on global-­‐to-­‐regional scales, in response to specific forcings, and test to what extent they explain divergence or convergence between different models. Whereas WP1 focuses on the analysis of exisHng coupled model integraHons forced by historical and projected future changes in radiaHve forcing, WP2 focuses on new model experiments designed specifically to elucidate key processes, creaHng process-­‐based fingerprints for WP4 ObjecHves: 1.  Formulate and test hypotheses regarding the processes that determine the water cycle response to specific forcings, and uncertainHes in the response. 2.  DisHnguish the roles of processes that are characterised by different Hmescales by studying the response to an abrupt change in forcing 3.  IdenHfy new candidates for process-­‐based fingerprints of the changing water cycle. MetUM global atmosphere/coupled model
climate configurations in use
Atmosphere/land
GloSea5
N320
N216
N144
N96
Joint Weather and Climate
Research Programme
UPSCALE
40km
60km
A partnership in climate research
N512
Current operational
NWP resolution
25km
N768
90km
130km
UK-ESM1?
17km
ORCA025
Texas
12km
0.25°
ORCA1
ORCA12
1°
0.08°
Ocean/sea-ice
Essentially the same
physics/dynamics
parameters used
throughout model
hierarchy
N1024
Planned
Explicit
convection
Project to
assess impact
of global explicit
convection
The energy budget as represented in AGCMs Albedo
30%
28 29 28 28
29 29 29 29
29 29
244 243 244 245
ASR
242 243 243 244
239.4
244 241
-0.2 0 0.6 0.5
Net TOA
-0.2 0.4 0.7 1.2
0.9
-1.5 -1.1
244 244 244 245
243 243 243 243
246 243
341 341 341 341
341 341 341 341.5
344 341
97 98 97 96
99 98 98 98
100 100
73 73 73 73
71 71 71 71
80 73
69 70 68 68
73 72 71 71
76 76
28 28 28 28
26 26 26 26
24 24
172 171 172 172
171 172 173 173
164 169
18 17 18 18
20 20 20 20
17 18
91 92 91 91
88 89 89 89
79 82
91 92 91 92
88 89 89 89
83 76
HadGEM1: N48 N96 N144 N216
HadGEM3: N96 N216 N320 N512
ERA-I MERRA
399 399 399 398
399 399 399 398
398 395
0.7 0.9 1.5 1.4
0.6 1.2 1.5 2.0
6.5 11.1
337 338 338 338
337 337 336 335
342 331
Energy balance at TOA and surface Globally too much surface SW radiaHon compared to observaHons and reanalyses Excess in latent heat and precipitaHon consistent with excess in OLR New esHmates of LW down: 345-­‐350 +/-­‐ 10 W m-­‐2 Adapted from Trenberth et al., 2009, 2011 Fluxes: W/m2 Demory et al, Climate Dynamics, submitted
How resoluHon affects the hydrological cycle: a study with atmospheric GCMs 11.8 12.2 12.3 12.5
12.0 12.1 12.1 12.0
12.5 12.7
32,29,29 42,37,37 47,41,41 54,49,48
41,36,35 49,44,43 51,45,45 50,45,44
36,44,36 40,-1,30
105 115 122 131
116 128 126 128
119 117
386
471 467 459 450
444 434 436 434
412 411
114
76 78 81 83
81 84 81 84
82 86
500 504 500 499
479 478 481 479
456 409
426
33 39 42 50
37 43 44 43
HadGEM1: N48 N96 N144 N216
HadGEM3: N96 N216 N320 N512
ERA-I MERRA
Adapted from Trenberth et al., 2007, 2011 74
Biases in climate models: too intense water cycle (P and E too high): common in IPCC models Water budget well balanced in climate models (not always the case in reanalyses) Moisture transport (ocean to land or land to ocean) way too low at low-­‐resoluHon Storage: 103km3, Fluxes: 103km3/year For water vapour transport from ocean to land: (i) atmospheric moisture convergence These 3 numbers should be equal (ii) E -­‐ P from the ocean (iii) P -­‐ E from the land Demory et al, Climate Dynamics, submitted
Over land ∇·Q
High local recycling Low transport ΔS
Lower local recycling Higher transport Robustness of results HadGAM1: 3-­‐member ensembles HadGAM3: 5-­‐member ensemble HadGAM1 & HadGEM3-­‐A: 2 different models with different water budgets Same tendency with resoluHon: convergence around N216-­‐N320 ResoluHon Trenberth et al., 2007 Demory et al, Climate Dynamics, submitted
The PRACE-­‐UPSCALE Project UK on PRACE -­‐ weather resolving SimulaHons of Climate for globAL Environmental risk •  current “numerical mission” of the JWCRP High-­‐resoluHon climate modelling team •  144 million core hours (equivalent to roughly half the HECToR facility) awarded for 1 year by PRACE (Partnership for Advanced CompuHng in Europe) •  HadGEM3-­‐A mulH-­‐decadal simulaHons at N96 (130 km) to N512 (25 km), shorter experimental simulaHons at N1024 (12 km) •  present climate simulaHons •  forced with OSTIA SSTs •  1985-­‐2011 (27 years) •  5 ensemble members, each 27 years •  future climate simulaHons •  3 ensemble members, each 27 years •  following RCP8.5 •  SST: daily OSTIA + HadGEM2-­‐AO RCP8.5 2100 ΔSST •  UPSCALE output available on JASMIN@CEDA Tropical cyclone track density (transits per month) UPSCALE: emerging processes and GCM high-­‐fidelity UPSCALE aims to increase the fidelity of global climate simulaHons and our understanding of weather and climate risk, by represenHng fundamental weather and climate processes more completely. This will enhance our confidence in projecHons of climate change, including extremes such as cyclones, heat waves, floods: • 
• 
• 
Extreme events impact society and yet most are absent in IPCC-­‐class climate models (see an example in the figure) These are rare events and require a large sample to be studied robustly UPSCALE uses a fine grid, similar to that used in global weather forecasHng, with a set of simulaHons for both current and future climates Strachan e et al., J. Clim., 2012 and Vidale et al. 2013, in preparation
IPCC class UPSCALE OBSERVATIONS First results at 25km: Europe MEAN 99% quanHle First results at 25km: sHll no monsoon MEAN 99% quanHle Local time of peak precipitation for 12km models
(diurnal cycle) – Mar-Feb 08/09
Conv param (GA4)
Shallow
22
23
0
1
21
20
19
3
4
5
18
6
17
7
8
16
Joint Weather
and Climate
9
15
10
14
13 12 11
paramResearch Programme
A partnership in climate research
Explicit convection
2
TRMM observations
WP3: Observed changes in the global water cycle Lead: R.Allan (Reading), S. Woolnough (Reading), R. Marsh (Southampton), S. Milton (Met Office) The overarching goal of WP3 is to characterise observed changes in the water cycle on global-­‐to-­‐regional space scales and decadal Hmescales, and to evaluate, where possible at a process level, the consistency between observed and modelled changes. ObjecHves: 1.  QuanHfy observed changes in the water cycle on global-­‐to-­‐regional space scales and decadal Hme scales and evaluate consistency with processes anHcipated by simple models and depicted by GCMs. 2.  Elucidate key regional processes and feedbacks relaHng to energy and water fluxes, ocean salinity, ocean surface heat flux tendencies and the parHHoning between land intercepHon, evaporaHon and transpiraHon. 3.  Monitor current observed changes in global water cycle variables, also employing global NWP forecasts to evaluate model drirs from analyses linking mean state errors to predicted hydrological response. How consistent are satellite precipitaHon datasets? TRMM3B42 has now been corrected Liu & Allan (2012) JGR 1 day 5 day PrecipitaHon intensity (mm/day) Observed P intensity & responses across datasets PrecipitaHon intensity change with mean surface temperature (%/day) (tropical oceans) Liu & Allan (2012) JGR PrecipitaHon intensity percenHle (%) Tropical precipitaHon variability in satellite data and CMIP5 simulaHons Note consistency between atmosphere-­‐only AMIP model simulaHons over land and GPCP observaHons. This is not the case for the ocean, in parHcular before about 1996. Oceans
Land Liu, Allan, Huffman (2012) GRL CMIP5 simulaHons: Wet regions get we#er, dry regions get drier Ocean Land Pre 1988 GPCP ocean data does not contain microwave data Robust drying of dry tropical land 30% we#est gridpoints vs 70% driest each month Liu and Allan in prep; see also Allan et al. (2010) ERL. Trends in P-­‐E and Surface Salinity •  Can we reconcile observed
changes in P-E & sea
surface salinity (SSS)?
•  What are the main drivers
of current trends in P and
SSS?
Southampton & Reading
Plot courtesy of Nikolaos Skliris (NOCS) Mean and changes (1979-2006): E-P and sea surface salinity (SSS)
Δ(E-­‐P) (E-­‐P) ΔSSS SSS •  evidence of intensifying hydrological cycle
PRACE SimulaHons with HadGEM3: 500TB of data to be analysed Summary •  IdenHfied some robust fingerprints of climate variability and change –  Assess model consistency vs. model error –  GCMs complementary to observaHons (incomplete) and re-­‐
analyses (do not close energy and water budgets) –  Currently applying observaHon-­‐based fingerprints to model analysis •  Role of emerging processes, e.g. water transport by large scale flow vs. eddies •  Role of physical parameterisaHons: apparently too large in low-­‐resoluHon GCMs •  Analysis of 500TB of data is extremely challenging: please help!