Understanding Climate Variability and Predictability using Models and Observations:

1. Understanding Climate Variability and Predictability using Models and Observations: Research at CGAM

2. NERC: Natural Environment Research Council • UK lead responsibility for developing, funding and delivering environmental science • To prioritise and deliver world-class environmental sciences to understand the Earth System. • Supports research, training and environmental observations across all components of the earth system • Uses a ‘whole system’ approach to earth system science to find sustainable solutions to environmental problems.

3. NERC Principle Science Areas • Earth’s Life-Support Systems: Water, biogeochemical cycles and biodiversity • Climate Change: Predicting and mitigating the impacts • Sustainable Economies: Identifying and providing sustainable solutions to the challenges associated with energy, land use and hazard mitigation

4. British Antarctic Survey (BAS) British Geological Survey (BGS) Centre for Ecology and Hydrology (CEH) Proudman Oceanographic Laboratory (POL) Southampton Oceanography Centre (SOC) Centre for Terrestrial Carbon Dynamics (CTCD) NERC Centres for Atmospheric Science (NCAS) Environmental Systems Science Centre (ESSC) Data Assimilation Research Centre (DARC) Tyndall Centre for Climate Change Research National Institute for Environmental e-Science (NIEeS) Centre for Polar Observations and Modelling (CPOM) NERC supports scientists within Universities and at its major core research centres:

5. Delivers and coordinates NERC’s core strategic research in atmospheric science Supports centres of excellence and facilities, distributed across many UK universities and related institutions Works closely with Meteorological Office/Hadley Centre and the UK Environment Agency NERC Centres for Atmospheric Science

6. Centres and Facilities • Centre for Global Atmospheric Modelling (CGAM): Climate processes, variability and change • Atmospheric Chemistry Modelling Support Unit (ACMSU): Chemistry-Climate modelling and interactions • Universities’ Weather and Environment Research Network (UWERN): High impact weather • Distributed Institute for Atmospheric Composition (DIAC): Laboratory and field studies of processes in the chemical and physical environment. • British Atmospheric Data Centre (BADC) • University Facilities for Atmospheric Measurements (UFAM) • Facility for Airborne Atmospheric Measurements (FAAM)

7. Centre for Global Atmospheric Modelling • To understand and simulate the highly non-linear dynamics and feedbacks of the global climate system • To exploit the revolution in seasonal to interannual prediction as a test bed of climate models and as a vehicle for fostering integrated applications • To capitalize on and develop NERC expertise in earth system science • To harness the expected increases in computer power and the opportunities provided by e-science to perform higher resolution, more comprehensive integrations of the earth system.

8. Typical questions that CGAM addresses are: • Will there be an El Nino this year and how severe will it be? • Will we have autumn floods like 2000 again? • Will the milder winters of the last decade or so continue? • Is there a risk of rapid climate change associated with THC shutdown? • Can we reproduce and understand past abrupt changes in climate?

9. Re-analyses of the global circulation Satellite Observations In situ Measurements To address these questions CGAM uses: 1. Observations of the climate: Essential for (i) describing variability of the current climate, (ii) finding associations between different parts of the climate system, (iii) evaluating climate model simulations.

10. Since the long-term memory of the system, required for predictability, resides in the oceans and land, CGAM uses state-of-the-art models of the complete system: 2. Models: Models are our laboratory. We use them to (i) investigate predictability, (ii) to explore forcing and feedbacks in the climate system, and (iii) to test hypotheses.

11. CGAM Principal Research Areas • Euro-Atlantic climate variability and predictability (Rowan Sutton, Brian Hoskins) • Mechanisms of climate variability and predictability using a model hierarchy (Brian Hoskins, Mike Blackburn) • Paleoclimate modelling (Paul Valdes) • Tropical climate variability and predictability (Julia Slingo)

12. Understanding the UK floods of Autumn 2000: Were they predictable? Autumn 2000 experienced heaviest rainfall on record Associated with persistent weather pattern driven from the tropics? Atlantic Ocean was also much warmer than usual. Did the ocean play a role? But the extreme rainfall was not captured by the seasonal forecasts. We need to understand why. Courtesy: Mike Blackburn, Brian Hoskins, CGAM

13. Does El Nino/La Nina influence UK climate? 1997:El Nino 1998: La Nina Global ocean forcing Without Atlantic forcing Implied effect of Atlantic • El Nino/La Nina affects the seasonal climate of the Atlantic and the UK in a potentially predictable manner. • The forcing from the Pacific dominates when El Nino/La Nina is strong.

14. Consider another year (1999) when El Nino/La Nina was weaker: So what role for the Atlantic? • In the absence of strong Pacific forcing, the state of the Atlantic Ocean is important for seasonal predictability. • Strong evidence that the Atlantic Ocean affects the climate of the UK and western Europe.

15. The THC describes the transport of heat by the global ocean circulation. The release of heat to the atmosphere over the Atlantic gives our relatively mild winters. Recent CGAM research has reinforced the association between changes in the strength of the THC and those in the NAO. We have shown that variations in the THC lead those in sea surface temperatures and the NAO by ~ 3 years. The Thermohaline Circulation (THC) and UK climate

16. Example of tracks and intensities (coloured dots) of cyclones during typical winter season (DJF) Note two major storm tracks over Pacific and Atlantic. Also secondary storm track over Mediterranean and extending eastwards. By producing a statistics of the tracks and intensities, various aspects of storm track behaviour can be diagnosed. Variations in the tracks and intensity of cyclones depending on the phases of the NAO, ENSO, for example, can also be studied. See: http://www.nerc-essc.ac.uk/~kih/TRACK/Track.html

17. Mean track density for DJF: 1979-95 ERA-15 HadAM3 Pacific storm track is too strong. Atlantic storm track too weak in HadAM3, possibly related to lack of systems coming off the eastern side of the Rockies.

18. Mean intensity of the cyclones for DJF: 1979-95 ERA-15 HadAM3 Patterns quite well simulated, although peak intensity generally underestimated in HadAM3. Resolution?

19. Mean genesis (source) regions for cyclones in DJF: 1979-95 ERA-15 HadAM3 Cyclones generated mainly by orography and in the baroclinic zones off the eastern seaboards. Note also secondary genesis over mid-ocean. Regions of genesis well captured by HadAM3, although the birth of cyclones along the eastern edge of the Rockies is inadequate.

20. 2002: A SUMMER OF DROUGHTS AND FLOODS Drought in India: India experiencing worst monsoon for over 30 years. Major drought conditions for NW India (Figure 1) Figure 1: Accumulated All India Rainfall (upper) and % departures from normal up to 21 August (lower). Figure 3: Hovmoller diagram of equatorial 200hPa velocity potential anomalies showing eastward propagation of the MJO. Orange is suppressed phase Figure 2: Daily All India Rainfall Associated with major break in monsoon during July (Figure 2), linked to suppressed phase of an exceptionally active MJO season (Figure 3) Indian Rainfall courtesy of ‘Monsoon On Line’: http://tropmet.res.in/~kolli/MOL/

21. Floods in Eastern Europe: A link to the Indian Monsoon? Rodwell and Hoskins (1999, QJRMS) identified a key link between the Asian Summer Monsoon (ascent) and dry weather over the Eastern Mediterranean (descent; Fig.3). Could a significant break in the monsoon influence Eastern Europe? Figure 3: Vertical motion at 500hPa generated by an idealised model in response to monsoon heating centred on 250N, 900E. Early results suggest that a link may exist (Fig. 4) between monsoon breaks and disturbed weather over eastern Europe, particularly in extreme events. Also noteworthy that extreme events tend to occur in years with El Nino conditions e.g. 1972, 1987? Figure 4: Timeseries of anomalies in All India Rainfall and subsidence over Eastern Europe (00-300E, 350N – 450N) during July. Correlation of r=0.39. Note monthly mean subsidence of ~0.05hPa and All India Rainfall of ~5mm/day Courtesy: Mike Blackburn (CGAM)

22. The MJO and coupling with the ocean: Observations(Woolnough et al., 2000: J. Clim., 13, 2086-2104) Observations show a coherent relationship between convection and SST. Warm SSTs precede convection by 5-10 days and are the result of weaker winds, reduced LH flux and increased SW flux during suppressed phases of the MJO.

23. The MJO and coupling with the ocean: Modelling(Inness and Slingo, 2002, J. Clim. In press) CGCM has a propagating convective signal compared with standing oscillation in AGCM. Coherent variations in SST in CGCM Coupling with the upper ocean is important for the MJO

24. BUT intraseasonal SST variations in CGCMs are too small and the MJO signal is still weak: Is the representation of the upper ocean adequate? Schematic showing formation of salt barrier layer Large freshwater flux sets up a salt stratified barrier layer so that a shallow mixed layer forms which can respond rapidly to flux variations, such as the diurnal cycle in solar radiation. The presence of this barrier layer can potentially provide much stronger local coupling in the warm pool region than is currently found in coupled models which do not resolve the detailed structure of the warm pool upper ocean. (From Anderson et al., 1996: J. Clim)

25. Typical window brightness (K) images showing scales of convective organization Note tendency for cloud clusters to congregate together to form super-clusters with multi-day life cycles e.g. Madden Julian Oscillation Self organization

26. Temporal behaviour of convection around the equator from window brightness temperature for Jan.-Feb. 1992 Note evidence of coherent propagation.

27. Space-time spectra showing the organization of convection in association with theoretical equatorial waves. Anti-symmetric Symmetric Inertio-gravity Mixed Rossby-gravity Inertio-gravity Kelvin Rossby MJO From Wheeler and Kiladis 1999: J. Atmos. Sci.

28. Space-time spectra from R30 version of GFDL model Note lack of organization, an error common to many GCMs. Lack of self-organization mechanism?

29. Evolving grid approach Example of application of adaptive mesh refinement (AMR) to tropopause fold event. AMR places the resolution where the situation demands it, in this case around PV filaments. Courtesy Dr. N. Nikiforakis, DAMTP

30. Probability distribution functions (PDF) of monthly mean SST and precipitation over the tropical Pacific: DJF (upper panels), MAM (lower panels) CMAP HadAM3 HadAM3-CMAP Note tendency for HadAM3 to overestimate precipitation over warm SSTs. PDF is also too tight, following closely the exponential relationship implied by the Clausius-Clapeyron equation for saturated vapour pressure.

31. HadAM3 ENSO Simulations • 6 member ensemble of HadAM3 forced with observed SSTs: 1870-1998 • Compare with NCEP Reanalyses • Composite El Nino events which have similar strength, evolution and seasonality. 5 events chosen: 1957/58, 1965/66, 1972/73, 1982/83 and 1997/98. Ref: Spencer and Slingo, 2002: Journal of Climate (Submitted)

32. Composite SST anomalies for El Nino events DJF at peak of El Nino MAM after peak of El Nino Note global patterns of coherent changes in SST. These are forced by the atmospheric response to the primary SST anomalies in the tropical Pacific.

33. Composite PMSL anomalies for DJF at peak of El Nino NCEP Reanalyses HadAM3 Note good simulation of tropical anomalies – the Southern Oscillation. Anomalies over N. Pacific show major errors with an in situ deepening of the Aleutian Low in HadAM3 rather than a shift eastwards and the development of a ridge over the north west Pacific.

34. Composite precipitation for DJF at peak of El Nino NCEP Reanalyses HadAM3 Note eastwards shift of precipitation maxima in NCEP reanalyses with reduced rainfall over the Maritime Continent. HadAM3 retains the precipitation maxima over the West Pacific, leading to the lack of eastwards shift in the Aleutian Low.

35. Understanding the influence of remote ocean response on ENSO teleconnections HadAM3 experiments: 10 realisations of each phase of El Nino/La Nina cycle Courtesy: Hilary Spencer

36. JJA after peak El Nino DJF at peak El Nino • At peak of El Nino, Indian Ocean SSTs have a significant effect on ENSO; tropical Atlantic and extra-tropical SSTs are less important. • After peak of El Nino, warming of tropical remote oceans, both Indian and Atlantic, significantly affects atmospheric response. • Remote ocean response to ENSO may lead to extended predictability. POGA IPOGA TOGA GOGA

37. Example of El Nino in Hadley Centre coupled models Normalised power spectra of Nino3 SST anomalies. Numbers in brackets indicate the standard deviation of the Nino3 timeseries Note tendency, common to many coupled models, for El Nino to occur too often and to be too regular. Also the temporal behaviour of El Nino (but not amplitude) appears to be insensitive to the ocean model used.

38. LAND BIOSPHERE REGIONAL CLIMATE MODEL DATA ASSIMILATION SYSTEMS ATMOSPHERE LAND SURFACE OCEAN CRYOSPHERE OCEAN BIOSPHERE Modular Earth System Modelling: A new approach for understanding the coupled system REGIONAL CLIMATE MODEL DATA ASSIMILATION SYSTEMS COUPLER ATMOSPHERIC CHEMISTRY SOCIO-ECONOMIC BIO- GEO- CHEMISTRY • The core of the model is the coupler which exchanges information between different components of the earth system. • CGAM has pioneered the use of such a structure in the UK and has demonstrated its value by interchanging ocean and atmospheric modules.

39. An Infrastructure Project for Climate Research in Europe • Involves current state-of-the-art atmosphere, ocean, sea-ice, atmospheric chemistry, land-surface and ocean-biogeochemistry models • 22 partners: leading climate researchers and computer vendors • Ultimate objective: Distributed European network for Earth System Modelling • See http://prism.enes.org • PRISM will: • Coordinate European Climate Modelling efforts: • Create a European service and management infrastructure for European wide, multi-institutional climate and Earth System simulations • Develop a European Climate Modelling System: • Portable, efficient and user-friendly + based on state-of-the-art models • + diagnostics and visualisation

40. Application of modular approach: Understanding coupled GCMs SINTEX HadOPA HadCM3 HadCEM GloSea … LMDz ECHAM T30/T42/T106 HadAM3 HadOM3 HadOM3 HadGOM OPA Common ocean Common atmosphere Different resolutions Intercomparisonidentify origin of errors

41. El Nino tends to be too regular and occur too often. Observations Model What controls El Nino in Coupled Climate Models? • Exchanging ocean models suggests that:- • atmosphere controls the periodicity • ocean controls the strength of El Nino BUT the use of a high resolution atmosphere (10) dramatically improves the temporal behaviour of El Nino and for the first time provides a more realistic simulation of the lower frequencies.

42. UK-HIGEM • A National Programme in ‘Grand Challenge’ • High Resolution Modelling of the Global Environment • To develop a high-resolution version (~ 10 atmosphere, 0.330 ocean) of the Hadley Centre Global Environment Model (HadGEM). • To evaluate the model by stringently testing it against observations and more sophisticated, very high-resolution models of the component parts. • To improve our understanding and predictive capabilities in global environmental variability and change, with particular reference to extreme events, interactions between different components of the climate system, and the potential for climate ‘surprises’. • To provide the modelling framework in which new developments in numerical methods and key processes in the atmosphere, ocean, cryosphere and land can be efficiently incorporated, leading to the creation of the next generation global environment model • To provide the background models required for the synthesis and interpretation of the wealth of in situ and satellite observations of the global environment, • To deliver more robust estimates of the regional impacts of climate change required to guide government policy.

43. Improved ocean dynamics and mixing with higher resolution Image from 1/80 version of OCCAM Ocean GCM showing salinity jets at a depth of 100m in the South Pacific where the South equatorial Current is blocked by a series of island groups. See: http://www.soc.soton.ac.uk/JRD/OCCAM/

44. Daily Max 2m Air Temperature Precipitation Soil Moisture Content Latent Heat Flux Sensitivity to seasonally varying vegetation phenologyNumber of months per yearLAI-Phen statistically different from LAI-Mean