* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download ILEAPS presentation
Global warming controversy wikipedia , lookup
Climate change denial wikipedia , lookup
Climate resilience wikipedia , lookup
Politics of global warming wikipedia , lookup
Michael E. Mann wikipedia , lookup
Fred Singer wikipedia , lookup
Global warming hiatus wikipedia , lookup
Climatic Research Unit email controversy wikipedia , lookup
Global warming wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate engineering wikipedia , lookup
Climate change adaptation wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Climate governance wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Solar radiation management wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Climate change feedback wikipedia , lookup
Effects of global warming wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change in the United States wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate sensitivity wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Climate change and poverty wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Land-atmosphere coupling, climate-change and extreme events + Activities with regard to land flux estimations at ETH Zurich Sonia I. Seneviratne Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland LandFlux Meeting, Toulouse, France May 29, 2007 Outline • Land-atmosphere coupling, climate change, and extreme events (Seneviratne et al. 2006) – Land-atmosphere coupling: hot spot in Europe? – Dynamics with climate change – Links with extreme events • Activities with regard to land flux estimations at ETH Zurich – Atmospheric-terrestrial water balance estimates – Some results on models’ estimates (land surface models, GCMs) – SwissFluxnet activities and Rietholzbach catchment site L-A coupling in Europe Seneviratne et al. 2006, Nature, 443, 205-209 L-A coupling in Europe Koster et al., 2004, Science L-A coupling in Europe No strong coupling in Europe? How about Mediterranean region? NB: Results based on only one year SST conditions (1994) Koster et al., 2004, Science L-A coupling in Europe T (Koster et al. 2006, JHM) No strong coupling in Europe? How about Mediterranean region? NB: Results based on only one year SST conditions (1994) Koster et al., 2004, Science Projected changes in To variability PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990) DT Ds/s Schär et al. 2004, Nature [%] [ºC] IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990) DT Ds Seneviratne et al. 2006, Nature, suppl. inf. Projected changes in To variability PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990) DT Ds/s Schär et al. 2004, Nature [%] [ºC] IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990) DT Ds Seneviratne et al. 2006, Nature, suppl. inf. Projected changes in To variability PRUDENCE, CHRM, JJA (2070-2100)-(1960-1990) DT Large changes in To variability Ds/s Schär et al. 2004, Nature [%] [ºC] IPCC AR4 GCMs, JJA (2080-2100)-(1970-1990) DT Ds What are the responsible mechanisms: Large-scale circulation patterns? Land surface processes? Seneviratne et al. 2006, Nature, suppl. inf. Land-atmosphere coupling experiment Aim: Investigate the role of land-atmosphere coupling for the predicted enhancement of summer temperature variability in Europe Approach: Perform regional climate simulations within the same set-up with and without land-atmosphere coupling for present and future climate conditions Summer temperature variability Standard deviation of the summer (JJA) 2-m temperature CTL CTLUNCOUPLED SCEN SCENUNCOUPLED (Seneviratne et al. 2006, Nature) Summer temperature variability Standard deviation of the summer (JJA) 2-m temperature CTL CTLUNCOUPLED SCEN SCENUNCOUPLED Most of the enhancement of summer temperature variability in SCEN disappears in the SCENUNCOUPLED simulation (Seneviratne et al. 2006, Nature) Climate change signal vs. LA coupling CLIMATE-CHANGE SIGNAL: SCEN-CTL CONTR. OF EXT. FACTORS TO CC SIGNAL SCENUNCOUPLED-CTLUNCOUPLED LA COUPLING STRENGTH IN SCEN: SCEN-SCENUNCOUPLED CONTR. OF LA COUPLING TO CC SIGNAL (SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED) Strength of land-atmosphere coupling in future climate is as large as 2/3 of the climate-change signal ! (Seneviratne et al. 2006, Nature) Climate change signal vs. LA coupling CLIMATE-CHANGE SIGNAL: SCEN-CTL CONTR. OF EXT. FACTORS TO CC SIGNAL SCENUNCOUPLED-CTLUNCOUPLED Contribution of landatmosphere coupling to climate change signal: dominant factor in Central and Eastern Europe! LA COUPLING STRENGTH IN SCEN: SCEN-SCENUNCOUPLED CONTR. OF LA COUPLING TO CC SIGNAL (SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED) (Seneviratne et al. 2006, Nature) GLACE results for present climate T GLACE experiment (Koster et al. 2004; 2006): no high land-atmosphere coupling in Europe neither for temperature nor for precipitation (Koster et al. 2006, JHM) P How is the strength of landatmosphere coupling for present vs. future climate in our simulations? (Koster et al. 2004, Science) Present vs. future climate percentage of To variance explained by coupling [%] s T2 (COUPLED) s T2 (UNCOUPLED) s T2 (COUPLED) land-atmosphere coupling strength parameter analogous to GLACE • Locally strong soil moisture-To coupling in present climate (Mediterranean; ≠GLACE) • Shift of region of strong soil moisture-To coupling from the Mediterranean to most of Central and Eastern Europe in future climate (Seneviratne et al. 2006, Nature) Comparison with IPCC AR4 GCMs Indirect measure of coupling between soil moisture & To: Correlation between summer evapotranspiration and temperature (ET,T2M) Negative correlation: strong soil moisturetemperature coupling (high temperature as result of low/no evapotranspiration) Positive correlation: low soil moisturetemperature coupling (high temperature leads to high evapotranspiration) Comparison IPCC AR4 GCMs: (ET,T2M) CTL time period SCEN time period Climate-change signal RCM 3 “best” GCMs All GCMs (Seneviratne et al. 2006, Nature) L-A coupling, Europe: present / future • Strong soil moisture-temperature coupling for the Mediterranean region in the CTL time period (≠GLACE) • Shift of region of strong soil moisture-temperature coupling to Central and Eastern Europe in future climate (transitional climate zone) • Qualitative agreement between RCM experiments and analysis of IPCC AR4 GCMs Soil moisture [mm] Mechanism for To variability increase Seasonal Cycle of Soil Moisture CTL (1961-1990) SCEN (2071-2100) Month no limitation wet climate transitional climate below threshold (“plant wilting point”) dry climate Summary • The projected enhancement of To variability in Central and Eastern Europe is mostly due to changes in land-atmosphere coupling • Climate change creates a new hot spot of soil moisture - To coupling in Central and Eastern Europe in the future climate (shift of climate regimes): Dynamic feature of the climate system! • LandFlux: Consider transient modifications with climate forcing (greenhouse gases, aerosols) Outline • Land-atmosphere coupling, climate changes, and extreme events (Seneviratne et al. 2006a) – Land-atmosphere coupling: hot spot in Europe? – Dynamics with climate change – Links with extreme events • Activities with regard to land flux estimations at ETH Zurich – Atmospheric-terrestrial water balance estimates – Some results on models’ estimates (GSWP/GLDAS-type; GCMs) – SwissFluxnet activities and Rietholzbach catchment site Atmospheric-Terrestrial Water Balance Atmospheric-Terrestrial Water Balance • Terrestrial water balance: Atmospheric-Terrestrial Water Balance • Terrestrial water balance: • Atmospheric water balance: Atmospheric-Terrestrial Water Balance • Terrestrial water balance: reanalysis data (ERA-40) • Atmospheric water balance: • Combined water balance: measured streamflow (Rs+Rg) Atmospheric-Terrestrial Water Balance The water-balance estimates depend only on observed or assimilated variables (≠ P,E) Main limitation: valid only for domains > 105106 km2 (Rasmusson 1968, Yeh et al. 1998) • Terrestrial water balance: reanalysis data (ERA-40) • Atmospheric water balance: • Combined water balance: measured streamflow (Rs+Rg) Case Study: Mississippi & Illinois Seneviratne et al. 2004, J. Climate, 17 (11), 2039-2057 corr=0.8, r2=0.71 Water-balance Estimates Observations (soil moisture+ groundwater+snow) Dataset for Mid-latitude River Basins “BSWB” Hirschi et al. 2006, J. Hydrometeorology, 7(1), 39-60 http://iacweb.ethz.ch/data/water_balance/ • divQ & dW/dt: whole ERA-40 period (1958-2002) • runoff data: Global Runoff Data Center (GRDC) Comparisons with soil moisture observations from the Global Soil Moisture Data Bank Volga River basin (1972-85) corr=0.8 r2=0.64 Atmospheric-Terrestrial Water Balance • Terrestrial water balance: • Atmospheric water balance: • Combined water balance: Estimation of large-scale ET Atmospheric water balance: Mackenzie GEWEX Study (MAGS) Peace Louie et al. 2002 Estimation of large-scale ET http://iacweb.ethz.ch/data/water_balance/ Retrospective dataset! (1958-2001, ERA-40; 2001-2007, ECMWF operational forecast analysis; e.g. Hirschi et al. 2006, GRL) Main limitations: - valid only for domains > 105-106 km2 (Rasmusson 1968, Yeh et al. 1998; Seneviratne et al. 2004, J. Climate, Hirschi et al, 2006, JHM) - Imbalances, drifts of reanalysis data The water-balance estimates depend only on observed P and assimilated variables Outline • Land-atmosphere coupling, climate change, and extreme events (Seneviratne et al. 2006) – Land-atmosphere coupling: hot spot in Europe? – Dynamics with climate change – Links with extreme events • Activities with regard to land flux estimations at ETH Zurich – Atmospheric-terrestrial water balance estimates – Some results on models’ estimates (GSWP/GLDAS-type; GCMs) – SwissFluxnet activities and Rietholzbach catchment site Precipitation Forcing for LSMs ( Koster et al, 2004: GPCP product, 1979-93) Oki et al 1999: a minimum of about 30 precipitation gauges per 106 km2 or about 2 gauges per 2.5o x 2.5o GPCP grid cell are required for accurate streamflow simulations Fekete et al. 2004: Range between 4 state-of-the-art precipitation datasets (CRU, GPCC, GPCP, and WillmottMatsuura) (Fekete et al. 2004) Effects on Catchment LSM Output r2 vs. ground data, yrs within 1979-93 (anomalies) Illinois Soil moisture + snow Volga Don Neva Ob Lena Dnepr Amur Precipitation Yenisei LSM results strongly dependent on quality of forcing... Modelling: GCMs Water-holding capacity LAND (Seneviratne et al. 2006, JHM) Modelling: GCMs Soil moisture memory (Seneviratne et al. 2006, JHM) Modelling: GCMs Soil moisture memory (Seneviratne et al. 2006, JHM) Modelling: GCMs Water-holding capacity LAND (Seneviratne et al. 2006, JHM) Modelling: GCMs Land-atmosphere coupling P Significant range in model behaviour… (Koster et al. 2004, Science) Outline • Land-atmosphere coupling, climate change, and extreme events (Seneviratne et al. 2006) – Land-atmosphere coupling: hot spot in Europe? – Dynamics with climate change – Links with extreme events • Activities with regard to land flux estimations at ETH Zurich – Atmospheric-terrestrial water balance estimates – Some results on models’ estimates (GSWP/GLDAS-type; GCMs) – SwissFluxnet activities and Rietholzbach catchment site Observations: FLUXNET • Worldwide CO2, water and energy flux measurements (integrating several projects such as AMERIFLUX, CARBOEUROPE, …) • At present, about 200 tower sites • however, still some serious limitations in temporal availability (in Europe, most measurements available after 1995 only) • only few sites with soil moisture measurements http://www-eosdis.ornl.gov/FLUXNET/ Observations: SwissFluxnet X Rietholzbach catchment site (Lysimeter, isotope measurements) Will also focus on soil moisture measurements (ETH Zurich) Outline • Land-atmosphere coupling, climate changes, and extreme events (Seneviratne et al. 2006a) – Land-atmosphere coupling: hot spot in Europe? – Dynamics with climate change – Links with extreme events • Activities with regard to land flux estimations at ETH Zurich – Atmospheric-terrestrial water balance estimates – Some results on models’ estimates (GSWP/GLDAS-type; GCMs) – SwissFluxnet activities and Rietholzbach catchment site • Conclusions and outlook Conclusions and outlook • Land processes important in transitional climate zones (e.g. Koster et al. 2004): seasonal forecasting, extreme events NB: possible changes in hot spots’ location with greenhouse warming • Several methods to estimate water storage or ET, atmospheric-terrestrial water estimates are promising (retrospective datasets) • No perfect dataset: but synergies are available Comparison: Land datasets Ground measurements Atmospheric waterbalance Satellite data (SMOS, GRACE) LSM with observed forcing Resolution Point measurements 300-1000 km (105-106 km2) SMOS: 40km GRACE: ~1000km 1km (LIS) - 50km Main advantage Ground truth (...) Retrospective dataset (1958present); large coverage Global coverage Good results in regions with good forcing; higher resolution Dependent on quality of convergence data (radiosonde vs. satellite data, drifts) Only recent data; short timeseries; products’ limitations (top soil, low res.) Results dependent on quality of forcing data; models optimized for regions with validation data Main limitation Point-scale measurements; limited temporal and geographical coverage Outlook A new GEWEX study area for Europe? (hot spot of coupling) Temporal Integration (3) Observations (Illinois) Integrated estimates S W t t Integration over longer time ranges is not straightforward due to the presence of small systematic imbalances in the monthly estimates Comparison with imbalances from other water-balance studies G97: Gutowski et al. 1997 Y98: Yeh et al. 1998 BR99: Berbery and Rasmuson 1999 Long-term Imbalances and Drifts (1) Hirschi et al. 2004 Illinois (1987-96) S t S W t t Imbalances (mm/d) ? Rasmusson (1968) threshold for radiosonde data (2.106 km2) Illinois (2 .105 km2) Europe Western Russia Asia North America Domain size (km2) Soil moisture - precipitation coupling CLIMATE-CHANGE SIGNAL: SCEN-CTL CONTR. OF EXT. FACTORS TO CC SIGNAL SCENUNCOUPLED-CTLUNCOUPLED • appears relevant for variability enhancement in the Alpine region LA COUPLING STRENGTH IN SCEN: SCEN-SCENUNCOUPLED CONTR. OF LA COUPLING TO CC SIGNAL (SCEN-SCENUNCOUPLED)-(CTL-CTLUNCOUPLED) • this link needs to be better investigated in future studies! (Seneviratne et al. 2006, Nature) Modelling Vegetation - CO2 interactions Only few models explicitly include vegetation-CO2 relationships… (enhanced water-use efficiency?, CO2 fertilization?) (Sellers et al. 1997) Modelling Vegetation - CO2 interactions Only few models explicitly include vegetation-CO2 relationships… (enhanced water-use efficiency?, CO2 fertilization?) (Sellers et al. 1997) Direct CO2 effect on runoff ? NPP, 2003 (Gedney et al. 2006, Nature) (Ciais et al. 2005, Nature) Soil moisture-temperature feedbacks Soil moisture-temperature coupling in the European summer 2003: Spring soil moisture impacted summer temperature by up to 2 oC! (Fischer et al. 2006, in preparation) Summer 2003 heatwave (Fischer et al. 2007, J. Climate, submitted) Summer 2003 heatwave (Fischer et al. 2007, J. Climate, submitted) Summer 2003 heatwave Dry or wet conditions in spring make up to 2oC difference in summer! (Fischer et al. 2007, J. Climate, submitted) Summer 2003 heatwave Dry or wet conditions in spring make up to 2oC difference in summer! (Fischer et al. 2007, J. Climate, submitted) Variability increases ∆s(P) vs. ∆s(To), PRUDENCE models (Central Europe) 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16) (Vidale al. 2006) DMI, HC1, HS1 DMI, HC2, HS2 HC, HadRM3H HC, HadAM3H, ens1 HC, HadAM3H, ens2 ETH/CHRM, HC_CTL, HC_A2 GKSS, HC_CTL, HC_A2 MPI, 3003, 3006 SMHI, HC_CTL, HC_A2 UCM, control, a2 ICTP, ref, A2 KNMI, HC1, HA2 CNRM, DA9, DE6 CNRM, DE3, DE7 CNRM, DE4, DE8 DMI, ecctrl, ecsca2‹ Observations: Soil moisture • Current ground observations networks of soil moisture are very limited in space and time (no data for Europe; only few observations in the former Soviet Union after 1990) Global Soil Moisture Data Bank (Robock et al. 2000, Bull. Am. Met. Soc.) Indirect measurements/estimates Some new approaches Satellite measurements • Microwave remote sensing (e.g. SMOS) • GRACE (Gravity Recovery and Climate Experiment) • NDVI (Normalized Difference Vegetation Index) GRACE twin satellites Land surface models with observational input • Global Soil Wetness Project (GSWP) • Global Land Data Assimilation (GLDAS) • Land data assimilation with Ensemble Kalman Filter (Reichle et al. 2002, JHM) Other applications Estimation of Large-scale Evapotranspiration: Atmospheric water balance: Mackenzie GEWEX Study (MAGS) Louie et al. 2002 Observations: Soil moisture • Current ground observations networks of soil moisture are very limited in space and time (no data for Europe; only few observations in the former Soviet Union after 1990) Global Soil Moisture Data Bank (Robock et al. 2000, Bull. Am. Met. Soc.)