Mer F - Climate Information Portals, Nairobi Aug 2012
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Mer F - Climate Information Portals, Nairobi Aug 2012
- 1. Introduction to the climate information portals Julian Ramirez / Andy Jarvis / Carlos Navarro / Flora Mer
- 2. Climate & Agriculture • Agriculture demands: – Multiple variables – Very high spatial resolution – Mid-high temporal (i.e. monthly, daily) resolution – High certainty – Both for present and future
- 3. Climate models with Evaluation of climate limited performance change impacts >> INCERTIDUMBRE ta of good Gaps in the climate Climatic da system representation quality Large uncertainties
- 4. Which climate data is used to assess agricultural impacts? Climate data sources GHCN PRESENT-DAY Local weather The most used GSOD stations data source WCL-WS GCM data Climate model RCM data GCM data more Satellite used than the outputs others. imagery WorldClim PROJECTIONS GCM at coarse resolution GCM data Downscaling to have better resolution (Ramirez and Challinor, 2012)
- 5. How to study the accuracy of climate model outputs? Comparison (R2 based) of interpolated climatology (WorldClim, The University of East Anglia Climatic Research Unit dataset (CRU)), and each of the GCMs (average 1961-1990 period) for each of the countries in the study area for mean temperature (left) and precipitation (right) for the annual mean. All R2 values were statistically significant at p < 0.001. (Ramirez and Challinor, 2012)
- 6. Projections of future global average annual precipitation for A1B scenarios from donwscaled data. 24 Global Circulation Models (GCMs) Uncertainties?
- 7. Projections of future global average annual temperature for A1B scenarios from donwscaled data. 24 Global Circulation Models (GCMs)
- 8. Downscaling by statistical Increase resolution, method or uniformity… Provide data with high dynamic Still the more resolution to assess method precise GCM impact studies on is too coarse agricultural systems, (100km). …
- 9. - For the whole world at Delta method 1km to 20km Statiscical - 20 GCMs for 2050, 9 downscaling for 2020 dowscaled to Dissagregation 20km, 5km, 1km PRECIS Dynamic …Which are downscaling Regional Climate CORDEX Model (RCM)
- 10. • Use anomalies and discard baselines in GCMs – Climate baseline: WorldClim – Used in the majority of studies – Takes original GCM time series – Calculates averages over a baseline and future periods (i.e. 2020s, 2050s) – Compute anomalies – Spline interpolation of anomalies – Sum anomalies to WorldClim
- 12. • Similar to the delta method, but does not use interpolation – Climate baseline: WorldClim – Calculate anomalies over periods in GCM cells – Sum anomalies to climate baseline
- 13. • Region: Andes • Resolution 50 km • Grid : 151 x 153 In Latin America
- 14. -The Coordinated Regional Downscaling Experiment in Africa- http://start.org/cordex-africa/
- 15. Method + - *Easy to implement * Change variable only at big scale Statistical * resolutions * Variables do not change their downscaling *Apply to all GCMs relations with time *Uniforme baseline * variables *Few platforms (PRECIS, CORDEX) * Robust *Many processes and stockages Dynamic *Apply to GCMs if data *Limited resolution (25-50km) downscaling available *Missing development * variables *Dificulty to quantify uncertainties
- 17. Our climate portal http://ccafs-climate.org
- 18. - Less access to internet - Data heavy to download http://ccafs-climate.org
- 19. • Improve baseline data and metadata • process and assess AR5 predictions (RCP 4.5) • Downscale with desired methods • Evaluate and assess uncertainties • Publish all datasets and results
- 20. • Downscaling is inevitable, so we are aiming to report caveats on the methods • Continuous improvements are being done • Strong focus on uncertainty analysis and improvement of baseline data • Reports and publications to be pursued… grounding with climate science
Notes de l'éditeur
- I need you to understand some key messages. Agriculture Demands to multiple variables like precipitation, temperature, wind speed, soil moisture, solar radiation, relative humidity, among many others. Agriculture demands very high spatial resolution, because agriculture is a niche-based activity Also, agriculture needs a Mid-high temporal resolution. For example monthly and daily data Both for present and future
- JRV: tambien podrias decir que Limitations es “lack of observed data”. Este es una redundancia del anterior. Tal vez podrias dejar solo este y TODA la info del anterior simplemente decirla (HABLAR). Datos climáticos De ellos dependen los cálculos de vulnerabilidad (incapacidad de un sistema para afrontar los efectos adversos del CC). Saber que va a pasar, cuando, para proyectar Planes de adaptación, Enfocar la investigación. Evaluación de impactos de cambio climático Dentro de este proceso se incluye: Desarrollar modelos → Conocer incertidumbres → Planes de acción → Generación de políticas Limitaciones Sistema climático complejo: Modelos todavía no pueden representar cientos de procesos de forma adecuada Resoluciones de modelos inadecuadas: Se requieren modelos con escalas finas. Incertidumbres: En cualquier agroecosistema, el clima es el fator menos importante. Importantes cuando se calculan impactos o se hacen estudios de vulnerabilidad. Incertidumbres en cuanto a futuras emisiones f(suposiciones concentraciones, población, desarollo econónico, tecnológico) Necesitamos saber Qué condiciones tendremos en 30, 50, 100 años? Inconvenientes Algunos vacíos Sistema climático complejo: Modelos todavía no pueden representar cientos de procesos de forma adecuada Resoluciones de modelos inadecuadas: Se requieren modelos con escalas finas. Incertidumbres: Importantes cuando se calculan impactos o se hacen estudios de vulnerabilidad. Futuras emisiones f(suposiciones concentraciones, población, desaroollo econónico, tecnológico) Objetivos Evaluar la capacidad del modelo MRI de representar clima presente. Evaluar las zonas donde es posible el uso del modelo. Analizar las incertidumbres del modelo.
- Para hacer una conexion entre este slide y el siguiente deberias decir que tanto los observed como las predicciones sufren de falta de calidad y de incertidumbres. En este sentido, debemos buscar como evaluarlos en calidad e incertidumbres… das click, y dices, por ejemplo, estos son modelos de IPCC Fourth Assessment Report, para 2 variables… etc etc… the Global Historical Climatology Network (GHCN, as in Section 3.3.1) version 2 (Peterson and Vose, 1997), available at http://www.ncdc.noaa.gov/pub/data/ghcn/v2. This database includes monthly historical totals (1900–2010) of precipitation (20,590 stations), and means of maximum, minimum (4966) and mean (7280) temperatures. (2) WCL-WS. The final dataset (after quality control and duplicates removal, see Hijmans et al., 2005 for more details) comprised 13,141 locations with monthly precipitation data, 3744 locations with monthly mean temperature, and 2684 locations with diurnal temperature range within our study region. This dataset is hereafter referred to as WCL-WS. (3) the Global Surface Summary of the Day (GSOD) was accessed at http://www.ncdc.noaa.gov/cgi-bin/res40.pl. This database contains daily data from ∼9000 weather stations worldwide for 18 variables, including, mean, maximum, minimum and dew point temperature, sea level and location pressure, visibility, wind speed and gust, pre-cipitation, snow depth, and specifications on the occurrence of rain, snow, fog, tornado, thunder, or hail (NOAA, 2011; ftp://ftp.ncdc.noaa.gov/pub/data/gsod/readme.txt). We selected weather stations within our study area (1999); aggregated daily rainfall, mean, maximum and minimum temperatures to a monthly time scale; and then averaged over the period 1961–1990. This dataset will be hereafter referred to as GSOD-CL
- JRV: cambie orden de slides JRV: remover texto abajo. Deja solo Ramirez-Villegas & Challinor (2012). Lo demas lo puedes decir (HABLAR) solo con “blue means model does well, red means model does not do well” Analyze climate model accuracy could help us to know which model to choose and which not regarding the correlation betwee each GCM and interpolated climate data (WorldClim). In East Africa, for mean temperature, there is a really good correlation between the 2 climate data sources. However, it seems more complicated for annual mean precipitation. For 2 GCMs, correlation between the 2 climate data source is really low in Ethiopia and Uganda.
- Different research institutes developed different global circulation models for each of the scenario projecting the global annual precipitation and mean temperature until 2100…
- JRV: Al final de este slide asegura decir “we have been working with downscaling of these projections… bla bla bla… we have a website.. Bla bla bla…”
- We need some options Even the most precise GCM is too coarse (~100km) Higher resolution is needed because 50 kmis not enough for agriculture To increase resolution, uniformise, provide high resolution and contextualised data
- JRV: cambie el orden JRV: remueve uno 10 u 11 Interpolation Delta The method basically produces changes in climate (deltas or anomalies) which is then added to the baseline Worldclim The method assumes that changes in climate are relevant only to thick scales and the relationships between variables are maintained into the future.
- Something like this.. As a result we have a surface that can reach 1 km spatial resolution.
- Similar to the delta method, but does not use interpolation
- The development of a surface from regional climate models We are working in
- CORDEX is a WCRP-sponsored program to organize an international coordinated framework to produce an improved generation of regional climate change projections world-wide for input into impact and adaptation studies within the AR5 timeline and beyond (visit the web of the kick-off workshop on evaluating and improving regional climate projections held in Toulouse). CORDEX will produce an ensemble of multiple dynamical and statistical downscaling models considering multiple forcing GCMs from the CMIP5 archive. Initially a 50 km grid spacing has been selected, favoring engagement of wider community. Multiple common domains covering all (or most) land areas in the World have been selected (with initial focus on AFRICA). These regions take advantage of existing regional projects (see the figure below). The Coordinated Regional Downscaling Experiment (CORDEX) program was recently established by the World Climate Research Program (WCRP). The aim of CORDEX is to develop an international coordinated framework for generating improved regional climate change projections worldwide. Results from the CORDEX analysis will be used as input to the IPCC Fifth Assessment Report as well as to meet the growing demand for high-resolution downscaled projections to inform climate change impact and adaptation studies. For Africa, CORDEX presents an unprecedented opportunity to advance knowledge of regional climate responses to global climate change, and for these insights to feed into on-going climate adaptation and risk assessment research and policy planning in the region. The keys to success of this initiative in Africa will be in developing a means for analysis and translation of CORDEX data in terms that are relevant to Africa’s knowledge needs, and in developing capacity within Africa to interpret, analyze, and properly apply downscaled climate model results from CORDEX.
- Resumen Dowscaling estadístico > Resoluciones (Se puede llegar incluso a 1km de resolución, 30seg) Aplicable a todos lo GCMs Rápidos de implementar < Variables Downscaling Dinámico + Robustos Depende de GCMs disponibles > Variables Mucho procesamiento y almacenamiento Downscaling estadístico o dinámico?? Ppal desventajas del downscaling estadístico o empírico : No tiene en cuenta los cambios en las fuerzas regionales. No obstante si se evalúa sistemáticamente x regiones la incertidumbre es grande xq la comparación con otros modelos es difícil. Dependen de relaciones entre variables. Calidad de datos históricos.
- At the moment there are already some of this information there Information flow is critical to us as feedback and not to repeat what others have done already.
- In Africa, our climate portal is less used. It could be because of a lower access to internet and/or because the data on the portal are really heavy to download. In the future, data will be in form of “gridbase” which will be much less heavy to download.
- Improve baseline data and metadata (incl. uncertainties) Gather and process AR5 projections (RCP 4.5 = representative concentration pathways). The dataset that we have at present are of Assesment report 4. Downscale with desired methods Evaluate (against weather stations) and assess uncertainties Publish all datasets (original and downscaled) and results
- Downscaling is inevitable, so we are aiming to report caveats on the methods Continuous improvements are being done Strong focus on uncertainty analysis and improvement of baseline data Reports and publications to be pursued… grounding with climate science
- JRV: deja solo un email: El tuyo o el de Carlos