1 Brendan Rogers. 2 SizePlacement of important currents.

Slides:



Advertisements
Similar presentations
The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information.
Advertisements

The Climate and the Human Activities The Climate and the Human Activities Natural Variations of the Water Cycle Natural Variations of the Water Cycle Water.
Scaling Laws, Scale Invariance, and Climate Prediction
Lucinda Mileham, Dr Richard Taylor, Dr Martin Todd
Mechanistic crop modelling and climate reanalysis Tom Osborne Crops and Climate Group Depts. of Meteorology & Agriculture University of Reading.
© Crown copyright Met Office Regional/local climate projections: present ability and future plans Research funded by Richard Jones: WCRP workshop on regional.
Consistency of recently observed trends over the Baltic Sea basin with climate change projections 7th Study Conference on BALTEX June 2013, Sweden.
Climate modeling Current state of climate knowledge – What does the historical data (temperature, CO 2, etc) tell us – What are trends in the current observational.
Downscaling and Uncertainty
3. Climate Change 3.1 Observations 3.2 Theory of Climate Change 3.3 Climate Change Prediction 3.4 The IPCC Process.
Dennis P. Lettenmaier Alan F. Hamlet JISAO Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering.
Progress in Downscaling Climate Change Scenarios in Idaho Brandon C. Moore.
Large-scale atmospheric circulation characteristics and their relations to local daily precipitation extremes in Hesse, central Germany Anahita Amiri Department.
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam WFM 6311: Climate Change Risk Management Akm Saiful Islam Lecture-4: Module- 3 Regional Climate.
Assessment of Future Change in Temperature and Precipitation over Pakistan (Simulated by PRECIS RCM for A2 Scenario) Siraj Ul Islam, Nadia Rehman.
The Introduction of a Knowledge-based Approach and Statistical Methods to Make GIS-Compatible Climate Map Goshi Fujimoto at MIG seminar on 16th, May Daly,
A Macroscale Glacier Model to Evaluate Climate Change Impacts in the Columbia River Basin Joseph Hamman, Bart Nijssen, Dennis P. Lettenmaier, Bibi Naz,
Impact of Climate Change on Flow in the Upper Mississippi River Basin
Simulations of Floods and Droughts in the Western U.S. Under Climate Change L. Ruby Leung Pacific Northwest National Laboratory US CLIVAR/NCAR ASP Researcher.
NCPP – needs, process components, structure of scientific climate impacts study approach, etc.
Regional Climate Modeling in the Source Region of Yellow River with complex topography using the RegCM3: Model validation Pinhong Hui, Jianping Tang School.
© Crown copyright Met Office Climate Projections for West Africa Andrew Hartley, Met Office: PARCC national workshop on climate information and species.
NARCCAP Users Meeting April 2011 Results from NCEP-driven RCMs Overview Based on Mearns et al. (BAMS, 2011) Results from NCEP-driven RCMs Overview Based.
Development of a downscaling prediction system Liqiang Sun International Research Institute for Climate and Society (IRI)
STARDEX STAtistical and Regional dynamical Downscaling of EXtremes for European regions A project within the EC 5th Framework Programme EVK2-CT
Uncertainty in climate scenarios when downscaling with an RCM M. Tadross, B. Hewitson, W Gutowski & AF07 collaborators Water Research Commission of South.
EGU General Assembly C. Cassardo 1, M. Galli 1, N. Vela 1 and S. K. Park 2,3 1 Department of General Physics, University of Torino, Italy 2 Department.
1 Climate Ensemble Simulations and Projections for Vietnam using PRECIS Model Presented by Hiep Van Nguyen Main contributors: Mai Van Khiem, Tran Thuc,
Climate Downscaling Using Regional Climate Models Liqiang Sun.
Regional climate prediction comparisons via statistical upscaling and downscaling Peter Guttorp University of Washington Norwegian Computing Center
Changes in Floods and Droughts in an Elevated CO 2 Climate Anthony M. DeAngelis Dr. Anthony J. Broccoli.
Downscaling and its limitation on climate change impact assessments Sepo Hachigonta University of Cape Town South Africa “Building Food Security in the.
© Crown copyright Met Office Providing High-Resolution Regional Climates for Vulnerability Assessment and Adaptation Planning Joseph Intsiful, African.
Reducing Canada's vulnerability to climate change - ESS J28 Earth Science for National Action on Climate Change Canada Water Accounts AET estimates for.
Henry Fuelberg Pete Saunders Pendleton, Oregon Research Region Map Types and Lightning Frequencies.
© Crown copyright Met Office Climate change and variability - Current capabilities - a synthesis of IPCC AR4 (WG1) Pete Falloon, Manager – Impacts Model.
Principal aspects taken into account in PRISM model: 1.Relationship between precipitation and elevation: Precipitation increases with elevation, with a.
Part I: Representation of the Effects of Sub- grid Scale Topography and Landuse on the Simulation of Surface Climate and Hydrology Part II: The Effects.
The climate and climate variability of the wind power resource in the Great Lakes region of the United States Sharon Zhong 1 *, Xiuping Li 1, Xindi Bian.
Assessing and predicting regional climate change Hans von Storch, Jonas Bhend and Armineh Barkhordarian Institute of Coastal Research, GKSS, Germany.
Climate Scenario and Uncertainties in the Caribbean Chen,Cassandra Rhoden,Albert Owino Anthony Chen,Cassandra Rhoden,Albert Owino Climate Studies Group.
Methods of Downscaling Future Climate Information and Applications Linda O. Mearns National Center for Atmospheric Research NARCCAP Users’ Meeting Boulder,
COST 723 WORKSHOP – SOFIA, BULGARIA MAY 2006 USE OF RADIOSONDE DATA FOR VALIDATION OF REGIONAL CLIMATE MODELLING SIMULATIONS OVER CYPRUS Panos Hadjinicolaou.
Assessing Global and Regional climate change scenarios for West Africa AIACC Project AF20.
Southern California February 9, 2002 MISR mesoscale climate dynamics in Southern California Sebastien Conil Alex Hall IRI, April 4, 2006.
Evapotranspiration Estimates over Canada based on Observed, GR2 and NARR forcings Korolevich, V., Fernandes, R., Wang, S., Simic, A., Gong, F. Natural.
Statistical Post Processing - Using Reforecast to Improve GEFS Forecast Yuejian Zhu Hong Guan and Bo Cui ECM/NCEP/NWS Dec. 3 rd 2013 Acknowledgements:
Mike Dettinger USGS, La Jolla, CA DOWNSCALING to local climate.
NARCCAP Meeting September 2009 Results from NCEP-driven RCMs ~ Overview ~ Results from NCEP-driven RCMs ~ Overview ~ William J. Gutowski, Jr. & Raymond.
© Crown copyright Met Office Downscaling ability of the HadRM3P model over North America Wilfran Moufouma-Okia and Richard Jones.
Ten-Year Simulations of U.S. Regional Climate Z. Pan, W. J. Gutowski, Jr., R. W. Arritt, E. S. Takle, F. Otieno, C. Anderson, M. Segal Iowa State University.
STARDEX STAtistical and Regional dynamical Downscaling of EXtremes for European regions A project within the EC 5th Framework Programme EVK2-CT
© Crown copyright Met Office Working with climate model ensembles PRECIS workshop, MMD, KL, November 2012.
Arctic climate simulations by coupled models - an overview - Annette Rinke and Klaus Dethloff Alfred Wegener Institute for Polar and Marine Research, Research.
NOAA Northeast Regional Climate Center Dr. Lee Tryhorn NOAA Climate Literacy Workshop April 2010 NOAA Northeast Regional Climate.
Consistency of recent climate change and expectation as depicted by scenarios over the Baltic Sea Catchment and the Mediterranean region Hans von Storch,
1/39 Seasonal Prediction of Asian Monsoon: Predictability Issues and Limitations Arun Kumar Climate Prediction Center
Regional Climate Change in the Pacific Northwest Eric Salathé Climate Impacts Group University of Washington With: Cliff Mass, Patrick Zahn, Rick Steed.
The North American Monsoons (NAM) can provide upwards of 70% of the annual precipitation to the southwest United States and Mexico. Already susceptible.
Global Circulation Models
Overview of Downscaling
Professor A.K.M. Saiful Islam
Question 1 Given that the globe is warming, why does the DJF outlook favor below-average temperatures in the southeastern U. S.? Climate variability on.
Mesoscale “Surprises” in Complex Terrain Revealed by Regional Climate Simulations Cliff Mass, Atmospheric Sciences University of Washington.
Image courtesy of NASA/GSFC
On HRM3 (a.k.a. HadRM3P, a.k.a. PRECIS) North American simulations
Aidan Burton, Stephen Blenkinsop, Vassilis Glenis, Claire Walsh,
Modeling the Atmos.-Ocean System
Presentation transcript:

1 Brendan Rogers

2 SizePlacement of important currents

3 1)Change factor, perturbation, or delta method (my data) 1)Regression methods (Gachon & Dibike, 2007) 1)Regional Climate Models (Pan et al., 2001) 1)Intercomparison/summary (Fowler et al., 2007)

4 1) Calculate GCM-simulated historical average 2) Calculate anomalies between future GCM and (1) 3) Apply anomalies to observed (e.g. PRISM) average Pros -rapidly applied to several GCMs/regions -relatively simple to implement Cons -assumes constant bias through time -assumes spatial pattern of climate will remain the same -assumes anomalies are the same regardless of elevation, continental position, etc. -generally ignores the change in variability(?) or extreme events, just scaling mean, max, and min of variables -can’t do local feedback effects “…sophisticated downscaling methods, despite the physically based concepts, are not yet completely satisfactory and they do not appear to be a good practical alternative to the perturbation method.” (Prudhomme et al., 2002)

03/09/11 What is Climate Mapping? The process of interpolating climate statistics at irregularly- spaced station locations to a regular grid “Geospatial Climatology” The study of the spatial patterns of climate on the earth’s surface and their causes

03/09/11 -Generates gridded estimates of climatic parameters (e.g., P, T, DP) -Moving-window regression of climate/weather values vs. elevation or climatology/radar for each grid cell -Uses nearby station observations - Spatial climate knowledge base weights stations in the regression function by their physiographic similarity to the target grid cell PRISM Parameter-elevation Regressions on Independent Slopes Model

03/09/11 Prism Interpolated Weather Data

03/09/11 New USDA Plant Hardiness Zone Map Products Guided by PRISM Climatologies New USDA Plant Hardiness Zone Map

9

10 May Average, May,

11 2.5

12

13 Observed May average tmp Hadley May 2058 tmp (observed + interpolated anomalies)

14 May, 2058

15 -study investigates regression-based SDSM model in its ability to simulate changes in mean temperature values, as well as extreme temperaturess, in northern Canada. -used 4 sets of climate predictors: A2 and B2 from CGCM2 and HadCM3 -SDSM was able to capture the temperature change signal, with plausible higher warming in winter than summer and in A2 vs. B2

16

17 Overview -Uses multiple regression to select from a list of potentially correlated variables at regional scales -Calibrates the regression model(s) against the observed data, using GCM output -Applies the model(s) to future GCM data Predictors to use -should have understandable physical relationship to the predictands -should avoid using GCM variables known to be inaccurate (here, surface temperatures) -GCMs are thought to give a more realistic description of the free tropospheric variables and large-scale circulation than of surface parameters -in northern Canada, the biases of 2-m GCM simulated temperature over inland or arctic seas can reach more than 12˚C in winter and fall - due to poor simulation of timing/magnitude of sea-ice extent and thickness -they used: 500 and 800 hPa geopotential heights, vorticity, zonal velocity component, specific humidity. The authors pride themselves on using specific humidity, because it represents a relatively independent predictor from the others to result in better accuracy.

18 -PROs -computationally inexpensive and thus can be applied to different data. -good for heterogeneous environments, like island or land/sea contexts, where there are strong relationships to synoptic-scale forcings CONs -assumes that statistical relationships developed for present day climate also hold in future. -Climate change proceeds on much longer time scales than those to which SD method is fit. -can’t do local feedbacks not captured by GCM

19

20 DownscaledRaw GCM

21 Downscaled tmax extremes

22 -using upper atmosphere predictors, the SD method produces temperature values more physically consistent with the current conditions and what can be expected in the future -the values are also more seasonally consistent and reduce inter-model differences -winters will warm more than summers -regression method is inherently conservative in the presence of non-stationarity and will underestimate the warming signal (not sure how they know that) -the local scale feedback of sea-ice retreat is not dynamically simulated but taken from the broader GCM, so it is missed here -they make the argument that SD passes the stationarity test because the same predictors and predictands (mostly) work in all stations for present day (substituting space for time)

23 -Two RCMs forced by 3 sets of initial boundary conditions for 10 years for US at 50 km resolution. -Three boundary conditions: ‘reanalysis’, GCM current climate, future scenario -RCMs do well in simulating orographic precipitation, E-W transcontinental gradients, and annual cycles. However, they miss the cool-season precipitation in the lower Mississippi River basin.

24 -Two RCMs: RegCM2, HIRHAM. -RegCM2: -18 categories of land use, 12 soil types -3 soil layers: top layer (10cm), root zone(1-2 m, depending on veg type), deep soil (10m). -14 vertical atmospheric layers based on pressure levels. -rainwater is not a predictive variable, so water rains out immediately with no re- evaporation -GCM used = HADCM2 -assumed 1% per year increase in GHGs after aerosol effects not included -simulation window corresponds to with 480 ppm -reanalysis (observations) came in at 28 pressure levels for

25

26

27 RCM performance bias: difference between reanalysis-driven RCM simulation and observations (depends on parameterization details, calibration, etc., models’ systematic errors and drift, and errors in re-analysis data) Boundary forcing bias: difference between RCM run driven by GCM current and by reanalysis (how the GCM is doing for the current climate) Intermodel bias: difference between runs from different RCMs, both driven by reanalysis data GCM-RCM downscaling bias: difference between GCM current and corresponding RCM driven by GCM output (includes potential improvement offered by RCM, as well as RCM errors)

28

29

30

31 Climate Change bias

32 -They used t-tests of separate means and variances to compute the statistical significance of climate change (2x10 years of simulations for each model). In most of the continental U.S., the t-value is larger than 6 (1.73 significant at 95% confidence level). It was only not significant in Texas/New Mexico, Northern Minnesota. Annual relative increase is 14-56% depending on the region. The dry west has larger increases in both relative and absolute magnitudes -GCM-RCM downscaling bias is by far the largest among all biases, implying a potential for RCMs to improve GCM simulations. -Bias analysis shows: 1) climate change is substantially larger than largest biases in several seasons and regions, 2) summer ratios of climate change vs. bias are always less than 1, and 3) the climate change:bias ratio is especially large in California. -in SE, RCM performance bias is relatively small, indicating that the GCM needs to provide better boundary conditions -West coast inter-model bias is largest in winter, suggesting that RCM interactions between onshore flow and topography need improvement.

33 Dynamical Downscaling -uses regional climate models (RCMs) or limited-area models (LAMs) -typically resolved at ~.5 degree latitude and longitude scale -parameterize physical atmospheric processes -can realistically simulate regional climate features such as orographic precipitation, extreme events, and regional scale anomalies, or non-linear effects such as El Nino -model skill depends strongly on biases inherited from driving GCM, presence/strength of regional scale forcings like orography, land-sea contrast, vegetation cover, and particular RCM -places with large topographic features (western US, Europe, New Zealand) report more skillful dynamical downscaling than places like Great Plains b/c regional forcings are stronger -variability in internal parameterizations also provides uncertainty (important and bad ones can include large-scale condensation and convection schemes, soil parameterization, lack of moisture advection into region, snow-albedo feedbacks)

34 Dynamical Downscaling -most RCMs are computationally expensive and only run for a few decades at a time – they will pick slices of time and just use ‘pattern scaling’: changes are scaled according to temperature signal modeled for intervening period, assuming linear rate of change -using an RCM can also add uncertainty to that inherent in the GCM output: -for temperature projections, uncertainty introduced by RCM is less than that from emissions scenario, but opposite is true for precipitation. -GCM output variability is generally larger than RCM variability -studies (Leung et al, 2004) have stated RCMs provide added value because the signals from RCMs can be significantly different from GCMs because of orographic forcing and rain-shadow effects. RCMs can also provide improved simulation of meso-scale precipitation processes -RCM model inter-comparisons are becoming more common.

35 Statistical Downscaling -all assume regional climates are a function of the large-scale atmospheric state -all use R = F(X), where R is local climate variable, X is set of large-scale climate variables, and F is function relating the two. F is typically derived by calibration using point observations or gridded reanalysis data. -validate using correlation coefficients, root mean square error, or explained variance (possibly low frequency variability important for climate change) -the trick is what set of variables, X, to use to predict R. This may not be easy and may not be stationary in time (ex. Humidity, pressure, to predict precip). Geopotential heights and specific humidity are found to be useful in all locations and seasons -choice of predictor domain also important (spatial/temporal extent of GCM outside of study area)

36 Statistical Downscaling -statistical methods are more straightforward than dynamical downscaling but tend to poorly represent extreme events and underestimate variance because only part of the regional and local climate variability is related to large scale variations -There are approaches to add variability: variable inflation and randomization -variable inflation: increases variability by multiplying by a suitable factor -randomization is variable inflation but with an added in the form of ‘white noise’ (found to give good results in European surface temperatures)

37