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The South East Asia Climate Analysis & Modelling project (SEACAM) David Hein, Climate Information Capability for International Development, Met Office © Crown copyright Met Office Contents • Background and project objectives • SEACAM activities • Literature review • Assessing user needs • Model configuration and experimental design • SEACAM results • Model verification - assessing historical simulations • Assessment of Mid- and Long-term Climate Change Projections • Summary and next steps © Crown copyright Met Office Background and project objectives © Crown copyright Met Office Geography and climate of Southeast Asia • Region expected to experience serious impacts of climate change (growing and urbanising population, reliance on climate-sensitive sectors e.g. agriculture, fisheries, natural resources). • Already affected by extreme weather events, particularly tropical cyclones, droughts and floods are expected to increase • Impacts of climate change not be evenly distributed - some regions may experience harsher impacts than others. • Policy-makers require reliable science and information to inform response • Limited studies and climate projects available for the region © Crown copyright Met Office Map showing the continental section of Southeast Asia on the top-left and the maritime section to its south and east. The SEACAM experiments • Primary goal was the production and analysis of climate projections for Southeast Asia • Planning workshop (June 2012,) brought together representatives from 8 of the 10 ASEAN member countries • Discussed the need for a dedicated project for Southeast Asia using a regional climate model (RCM) • RCM would be driven by several global models. Attendees decided to share the work load required to run the models • Six 150-year (1949-2099) “PRECIS” regional climate model experiments would be run, nicknamed the “DURIAN” experiments Trainers and participants at the June 2012 workshop in Singapore. • SEACAM would jointly analyse the 900 years (150 years times six experiments) of climate model output data from the DURIAN experiments Acknowledgements Project partners • Project supported through a Memorandum of Understanding between the National Environment Agency of Singapore and the Met Office • Memorandum of Understanding promotes intercountry cooperation for climate change research for Southeast Asia • Co-funding provided by the UK Foreign and Commonwealth Office - “Southeast Asia Prosperity Fund” • 19 project partners from Meteorological institutions and academia across Southeast Asia © Crown copyright Met Office Raizan Rahmat (report coordinator), Boonlert Archevarahuprok Chai Ping Kang, Chung Jing Xiang, David Hein, Dodo Gunawan, Erasmo Buonomo, Grace Redmond, Haji Sidup , Haji Sirabaha, Han Swe, Itesh Dash, Jacqueline Lim See Yee, Jose Rizal, Kornrawee Sitthichivapak, Kumarenthiran Subramaniam, Lalit Dagar, Liew Ju Neng, Ling Leong Kwok, Lyhon Ho, Mai Van Khiem, Matt Palmer, Monichoth So Im, Muhammad Yunus Ahmad Mazuki, Ngai Sheau Tieh, Ngo Duc Thanh, Saiful Azmi bin Haji Husain, Shamsuddin Shahid, Simon Tucker, Srivatsan V. , Suppakorn Chinvanno, Thelma Acebes Cinco, Thuy Tran Thanh, Vo Van Hoa, and Wittyi Soe 1 Thailand Meteorological Department (TMD) 2 Universiti Teknologi Malaysia (UTM) 3 Universiti Kebangsaan Malaysia (UKM) 4 Met Office Hadley Centre (MOHC) 5 Badan Meteorologi, Klimatologi, dan Geofisika, Indonesia (BMKG) 6 Brunei Darussalam Meteorological Department 7 Department of Meteorology and Hydrology, Myanmar (DMH) 8 Regional Integrated Multi-Hazard Early Warning System (RIMES, Thailand) 9 Centre for Climate Research, Singapore (CCRS) 10 Malaysian Meteorological Department (MMD) 11 University Brunei Darussalam (UBD) 12 Vietnam Institute of Meteorology, Hydrology and Environment (IMHEN) 13 Department of Meteorology, Cambodia 14 University Malaya (UM) 15 Hanoi University of Science 16 Tropical Marine Science Institute (TMSI), National University of Singapore 17 Southeast Asia START Regional Center, Thailand 18 Philippines Atmospheric, Geophysical & Astronomical Services Administration 19 National Center for Hydro-Meteorological Forecasting (NCHMF), Vietnam SEACAM activities © Crown copyright Met Office Project activities Inception (May 2011June 2012) • Agree need for RCM ensemble • Design RCM experiments • Run DURIAN expeiments Planning of Analysis and research • Literature review • End user survey • Workshop planning Review workshop (Cambodia August 2013) • 5 days • Analysis of results in 4 key areas • Agree further analysis Final workshop (Singapore February 2014) Photos from the first review workshop in August 2013 in Phnom Penh, Cambodia. © Crown copyright Met Office • Review outputs • Produce guidance • Final report (April 2014) Literature review © Crown copyright Met Office SEACAM Literature review • Identified a lack of regional downscaling modelling experiments in Southeast Asia • Determined that the IPCC AR5 contained the most comprehensive assessment of Southeast Asian climate to date: • AR5 indicates an increase in median temperature (0.8 – 3.2 degrees) and moderate increase in precipitation (1-8%) • AR5 provides various regional variations and low confidence assessment of circulations features and climate extremes • AR5 recommended that ensemble simulations using additional Regional Climate Models driven by other General Circulation Models should be carried out © Crown copyright Met Office Intergovernmental Panel on Climate Change: 5th Assessment Report • Warming is very likely to continue with substantial sub-regional variations. • Medium confidence in an moderate increase in rainfall over continental Southeast Asia. • To the south there is generally a drying although this may not be significant relative to the natural decadal variations in this region. Temperature change for Southeast Asia (land areas only) during JuneAugust, from CMIP5 global models for the four RCP greenhouse gas concentration levels. • Strong regional variations are expected because of terrain. • Extreme heavy rainfall events are projected to increase across the whole region. © Crown copyright Met Office Precipitation change for S.outheast Asia (land areasonly) during AprilSeptember, from CMIP5 global models for the four RCP greenhouse gas concentration levels. Results for precipitation are not clearly distinguishable among the RCPs. IPPC AR5 - Percentage changes in temperature and precipitation for Southeast Asia Percentage changes in temperature and precipitation for Southeast Asia according to model in the CMIP5 ensemble using RCP 4.5 (medium level greenhouse gas concentrations). © Crown copyright Met Office A little about Regional Climate Models……. © Crown copyright Met Office Resolution is important (example) © Crown copyright Met Office Resolution is important (example) © Crown copyright Met Office What is a Regional Climate Model (RCM)? • Covers a limited area of the Earth’s surface instead of the entire Earth • Like Global Circulation Models (GCMs), RCMs contains representations of the atmosphere, land and surface, and generate weather (and therefore climate) • Global models typically having a horizontal resolution of between 250 and 600 km © Crown copyright Met Office What is a Regional Climate Model (RCM)? • Their main advantage is that they allow for higher resolution climate modelling - . In most cases, higher resolution = more useful and higher quality information • For example, the HadRM3P used in Precis can be run a two resolutions 50km and 25km © Crown copyright Met Office RCM’s: about the input data … To obtain the global influence on regional climate RCMs take input at the boundaries (the edges of the region) from GCMs or reanalysis experiments © Crown copyright Met Office Limited area models are driven at the boundaries by GCMs or observations. Assessment of End User needs © Crown copyright Met Office Stakeholder Engagement • A survey was conducted to identify potential user needs of regional climate projections • 41 respondents from 25 agencies and research institutes • 54% hydrology sector, 41% ocean/marine, 39% agricultural • Most commonly required climate variables were: Surface air temperature, Large scale precipitation, Surface winds Convective precipitation © Crown copyright Met Office Respondents' area of research or work. Note that percentages do not add up to 100% as some respondents were involved in multi-faceted work areas. • “... The need for greater access to various types of climate information products is immense; however, capacity to interpret and correctly use the information is low.” --comment from a survey participant © Crown copyright Met Office Qualitative responses....notable responses included......... ““... assess the impact of climate change on its water resources in order to manage it in sustainable way...” “... alarming change in the past decade with respect to climate and its effects on agriculture, fisheries, drought. But proper training in using/running these models are missing. Appreciate if you can conduct a training in Southeast Asia ...” “... sea level rise, coastal erosion, (flash) flooding, landslides, groundwater salinisation, increasing temperature and warm nights/days ...” “ ... impact on rainforest, haze...” “ ... The land-use change, increasing temperatures and erratic pattern of weather ...” “... agricultural, fisheries influence of climate change, food security, renewable energy ...” “... we need adaptation strategy ...” “... users need to truly know the meaning of climate projections so as to avoid making wrong decisions ...” “... variables must demonstrate sufficient validity (bias correction, ability to reasonably replicate seasonality, etc.). Merely uploading variables without sufficient user education is dangerous...” “... change in rainfall patterns and inter-annual variability...” “ ... change in tropical cyclone numbers, ENSO, NE Monsoon...” “ ... cold spells, hot spells ...” © Crown copyright Met Office Model configuration and experimental design © Crown copyright Met Office SEACAM Experimental Design • Key considerations for design of the experiments included: • Regional model type used • Domain and Resolution • Driving data and emission scenarios • Time periods for analysis • Observational data used in evaluation • SEACAM uses the HadRM3P regional climate model used in PRECIS © Crown copyright Met Office The Components of PRECIS • PC version of the Hadley Centre’s HadRM3P Regional Climate Model • Horizontal resolution of 50km or 25km • runs on the free Linux operating system • Easy to use Graphical User Interface to set up RCM experiments • Data processing and analysis software • Boundary conditions (input data) • Training workshop and materials • Technical Support (forum.precisrcm.com) • The PRECIS web site and email address: http://www.metoffice.gov.uk/precis [email protected] © Crown copyright Met Office The domain initially proposed. Too Big! • Careful consideration of size and location of domain is required • SEACAM originally hoped to use a domain that included all ASEAN countries • However at this scale the model runs take too long to simulate © Crown copyright Met Office The Final Domain • It became necessary to reduce eastern extent of the domain to 135 degrees • West Papua region of Indonesia excluded in final domain • Proposed domain sent to participants to ensure all grid Resolution: 0.22°islands or boxes containing coastal *cities represented as (~25km 25km) land points and not ocean 208 by 200 grid points West Papua is used in DURIAN experiments. Blue indicates that the grid box is an ocean grid box. Green indicates that the grid excluded Domain box is land. Political boundaries are marked with red lines. The darker rim between the edge of the picture and the orange line is where the lateral boundary conditions (LBCs) are applied - output in this region is not analysed. © Crown copyright Met Office Driving data and emission scenarios © Crown copyright Met Office Driving data and emission scenarios • IPCC Special Report on Emissions Scenarios • Provides representations of future levels of substances that influence the total energy/heat in the atmosphere (e.g. greenhouse gases) or which can affect heatcontributing atmospheric substances (e.g. sulphur dioxide, which forms sulphate aerosols) • Four families/groups of scenarios (A1, A2, B1, B2) were identified • Scenarios are based on a coherent and internally consistent set of driving forces such as demographic and socio-economic developments © Crown copyright Met Office The IPCC SRES scenarios Four families identified: - A1: globalization, emphasis on human wealth. Globalized, intensive (market forces) - A2: regionalization, emphasis on human wealth. Regional, intensive (clash of civilizations) - B1: globalization, emphasis on sustainability and equity. Globalized, extensive (sustainable development) - B2: regionalization, emphasis on sustainability and equity. Regional, extensive (mixed green bag) © Crown copyright Met Office IPCC scenario A1B selected In SEACAM, the A1B scenario is used for all experiments. SRES A1B assumes: • Rapid economic growth. • A global population that reaches 9 billion in 2050 and then gradually declines. • The quick spread of new and efficient technologies. • A convergent world - income and way of life converge between regions. Extensive social and cultural interactions worldwide. • A balanced emphasis on all energy sources (i.e. between fossil and non-fossil). © Crown copyright Met Office Total global cumulative CO2 emissions (GtC) from 1990 to 2100 Driving data choice • Models must be able to represent processes that are smaller than the area of the model grid box (e.g. Clouds). • Many of these small scale processes are important for climate, but we are not certain about their exact values. For example, we know snowflakes fall at between 0.5 and 1.0 metres per second. • In a Perturbed Physics Ensemble (PPE), a large number of model simulations are run that sample the ranges of these small scale processes. • Five GCMs from the HadCM3Q PPE were downscaled: Q0, Q3, Q10, Q11 and Q13. These were selected as these members were deemed suitable to span the range of future possible outcomes. • The Max Planck Institute ECHAM5 was also selected in order to provide some element of a multi-model ensemble (models from more than one institute). © Crown copyright Met Office Observational data used for evaluation • To evaluate surface air temperature and precipitation, two different gridded datasets (APHRODITE and CRU-TS3) were used • CRU-TS3: monthly mean data at a spatial resolution of a 0.5° square grid. • APHRODITE: daily mean precipitation and temperature on a 0.25° square grid • Inclusion of daily mean data allows evaluation of extreme weather events • APHRODITE has a denser network of stations than CRU areas © Crown copyright Met Office Time periods and approach to analysis Time periods • Global models in SEACAM were downscaled by HadRM3P from 1950-2100 Analysis The evaluations were done in four broad categories (also termed “work packages”) as follow: Two key periods selected 1. Annual cycle of temperature and precipitation • 2071-2100 chosen to provide strong detectable signal for any possible climate changes on a longer term 2. Mean temperature and precipitation • 2031-2060 chosen to inform future policy relevant for adaptation planning © Crown copyright Met Office 3. Circulation patterns during the Northeast and Southwest Monsoons, and 4. Extreme precipitation and temperature. SEACAM results - Model Validation © Crown copyright Met Office Annual temperature cycle (surface air temperature at 1.5m) in the RCM experiments plotted against data from historical observations © Crown copyright Met Office Annual temperature cycle (surface air temperature at 1.5m) versus Observed © Crown copyright Met Office Annual temperature cycle assessment • In general, all the downscaled climate model simulations are able to simulate the temperature cycle well by capturing the observed peaks and dips across the year • This is especially true for countries in the mainland Southeast Asia region which includes Cambodia, Laos, Myanmar, Thailand, and Vietnam, as well as the Philippines • A general feature of the RCM simulations over these regions, with the exception of the Philippines, is the over-prediction (warm bias) of the warm months and the underprediction (cool bias) of the cooler months • Countries closer to the equator such as Brunei, Indonesia, Malaysia, Singapore and East Timor, the 6 RCM simulations show larger variability in comparison • These simulations under-predict for Brunei (with respect to APHRODITE; but not in relation to CRU) and over-predict for Singapore throughout the year (with respect to both APHRODITE and CRU). • As for Malaysia and Indonesia, the biases are GCM-dependent • Overall, simulations generate biases that range from 1.0 to 2.0°C. © Crown copyright Met Office Annual precipitation cycle precipitation in the RCM experiments plotted against data from historical observations © Crown copyright Met Office Annual precipitation cycle “...the precipitation cycles, or lack thereof, for Brunei, Malaysia and Singapore are poorly captured in the RCM.” © Crown copyright Met Office Annual precipitation cycle assessment • Annual rainfall cycles of the simulations are evaluated in a similar manner to the temperature cycle against APHRODITE and CRU for precipitation • For precipitation cycle, Southeast Asia can generally be divided into 3 sub-regions; • North where the Southwest Monsoon is dominant during the middle part of the year • Middle near the equator (i.e. Brunei, Malaysia and Singapore) • South (i.e. Indonesia and East Timor) where the wet and dry seasons are mirror images of the counterparts in the north. • Comparison of RCM simulations in these 3 regions shows the simulations’ ability in capturing large scale seasonal (monsoonal) rainfall in the northern latitudes of the region stands out compared to capturing thunderstorm development in the lower latitudes • The results for regions in the north and south are encouraging with the RCM simulations able to pick out the seasonal rainfall maxima during the June-September season (JJAS) for Cambodia, Laos, Myanmar, Philippines, Thailand and Vietnam and during the NovemberApril season (NDJFMA) for Indonesia and East Timor. • In contrast, the precipitation cycles, or lack thereof, for Brunei, Malaysia and Singapore are poorly captured in the RCM © Crown copyright Met Office Evaluation of seasonal spatial distributions • Evaluation of seasonal spatial distribution of temperature (and also rainfall) across the domain provides additional information over the evaluation for annual cycle, which has been spatially averaged. E.g. details of the location of errors, unlike the annual cycle. • Analysis was made for seasonal mean temperature, seasonal minimum and maximum temperature and seasonal spatial rainfall distribution Difference between the spatial distribution of simulated and observed DJF seasonal maximum temperature (°C). Red shades show warm biases , blue shades show cool biases . © Crown copyright Met Office Evaluation of seasonal spatial distribution of mean temperature • Generally, the mean seasonal temperature biases range between ±4°C with considerable spatial and seasonal variations. • Biases largely positive (warm biases) during the MAM season especially over the mainland while during the SON season, these generally negative (cool biases) • Broadly consistent with findings from the evaluation of the annual cycle in temperature • Model produced cooler climate over the NW edge of the domain (mountainous areas) • Generally patterns in RCM closely resemble ERA-40 Difference between the spatial distribution of simulated and observed June July August seasonal maximum temperature (°C). © Crown copyright Met Office Evaluation of seasonal minimum and maximum temperature • Generally, spatial bias structure of the seasonal maximum temperature is similar to the seasonal mean temperature • Maximum temperature are generally colder near the equatorial region with consistently largest bias (~4°C) over the west-coast of Sumatra. • Mainland Southeast Asia region, simulations are warmer than the observations, except at the centre of the region near ~20°N. • Large warm biases are ALSO noted over Mainland especially during MAM • All the HadCM3Q simulations produce biases patterns which resemble that of the ERA-40 simulations (suggesting biases are largely sourced from process-representations of RCM) Difference between the spatial distribution of simulated and observed March April May seasonal maximum temperature (°C). © Crown copyright Met Office Evaluation of seasonal spatial rainfall distribution • Generally, the bias patterns of seasonal rainfall are much noisier than temperature. • Simulations produce moderate wet biases of about 20-40% through the years, except over the western part of the mainland Southeast Asia where the biases are largely negative. • Close to the northern boundary of the simulations, the model errors remain large, suggesting influence of the boundary forcing over this region with steep and complex terrain. • Generally, RCM simulations show consistent biases patterns to ERA-40 simulations. • ECHAM5 simulations produced larger wet biases over Equatorial Maritime Continent (Sumatra and Borneo) during JJA Difference between the spatial distribution of simulated (and observed (MAM) seasonal rainfall in % of individual grids’ observed climatology. Red shades show dry biases of simulations, while blue shades show wet biases of simulations. © Crown copyright Met Office Evaluation of the Southwest Summer Monsoon • The RCM reasonably simulates the spatial pattern of the summer monsoon circulation. • However, there is a clear systematic positive bias in wind speed over the region in all model simulations. Land-only precipitation time-latitude cross-section averaged over longitudes 90E to 135E for APHRODITE, ERA-40 simulations, and 6 RCM simulations 850 hPa mean wind speed in m/s and direction during July, averaged over the 1971-2000 period for ERA-40 reanalysis and 6 RCM simulations • The precipitation distributions over time in the model runs compare well to APHRODITE observations during the southwest summer monsoon. • All the models are slightly wetter than observations. © Crown copyright Met Office Evaluation of the Northeast Winter Monsoon • Monsoon circulation - Comparison (ERA40 reanalysis and models) show monthly mean of meridional wind (1970-2000) are in general agreement, however intensities and the durations differ. • The upper level flow which indicates the returning branch of the Hadley Cell is captured in all the simulations Meridional wind component in m/s at 850 hPa averaged over the latitudes 13S to 30N over the 1971-2000 period for ERA-40 simulations and 6 RCM simulations. Red shades indicate southerly winds, blue shades indicate northerly winds. • Precipitation extreme - Spatial patterns of circulation In general the main precipitation pattern for the Northeast Monsoon is well captured in the projections. The heavy rainfall during December to March is indicated in all the RCM simulations. 95th percentile of DJF daily rainfall amounts for APHRODITE, ERA-40 simulations, and 6 RCM simulations. © Crown copyright Met Office Evaluation of extreme rainfall indices • Annual maximum one day rainfall (Rx1day) - RCM simulations showed consistent spatial patterns of biases with the ERA-40 simulations and with each other, which suggests biases can be reasonably attributed to the RCM itself • Annual maximum consecutive five days rainfall (Rx5day) - All the RCM simulations reproduce the basic pattern of variation estimated from the APHRODITE dataset over the whole area. However, the five HadCM3Q simulations show consistent bias patterns to that produced by the ERA-40 simulations, except for ECHAM5 simulations which show a more intense rainfall in the lower latitudes. • Annual maximum of consecutive dry days There is generally good agreement in the spatial patterns of maximum CDD (or the longest dry spell)in the ERA-40 simulations and 6 RCM simulations, with APHRODITE • Trends in extreme rainfall indices - Overall, the RCM was able to reproduce the sign of the trends (for Rx1day and Rx5day) and inter-annual variability of the observed rainfall indices Trends in APHRODITE and ERA-40 simulations of rainfall indices. © Crown copyright Met Office SEACAM results - Assessment of Midand Long-term Climate Change Projections © Crown copyright Met Office Assessment of Mid- and Long-term Climate Change Projections • Projections provided for two 30-year periods, “mid-term” 2031-2060 and “long term” 2071-2100. • Baseline period simulations) is 1970-2000 (as for • Projections assessed for: Annual temperature cycle Annual precipitation cycle Seasonal mean temperature Seasonal minimum temperature Seasonal maximum temperature Diurnal temperature range Seasonal mean rainfall Southwest Summer Monsoon Northeast Winter Monsoon Extreme rainfall indices Extreme temperature indices Five-year return level for maximum daily temperature and precipitation • A summary of key findings is provided. Full analysis available in final report © Crown copyright Met Office Key findings: Climate Change Projections Attribute Analysis / findings Annual temperature cycle In general, surface air temperature is expected to rise by 2°C by midcentury and 4°C by end-century, with some countries projected to be experiencing an increase by up to 5°C. In contrast , the projections for precipitation show a lot more variations across countries and seasons. Annual precipitation cycle Seasonal mean temperature Seasonal mean temperature shows an increase of 3-5°C by the endcentury. By mid-century, the estimated temperature is 2-4 °C warmer than the present day. Seasonal minimum Generally, the warming patterns of the seasonal minimum temperature temperature closely resemble that of the mean temperature. Seasonal maximum temperature Diurnal temperature range © Crown copyright Met Office Generally, the warming patterns of the seasonal maximum temperature (like the seasonal minimum temperature) closely resemble that of the mean temperature except that the warming rate is higher in the maximum temperature towards the end-century. Generally, the minimum temperature (night time) warms faster than the maximum temperature (day time) during the northern hemisphere winter, except over northern Borneo and Peninsular Malaysia. Key findings: Climate Change Projections Attribute Seasonal mean rainfall Analysis / findings Projections show drier climate over the sea and wetter climate over land. The land-sea contrast is more obvious towards the end-century. In all of the HadCM3Q projections, drier climate is projected over most areas during northern hemisphere winter except central mainland Southeast Asia. Wetter climate was projected south of the equator in ECHAM5. Southwest Summer Monsoon It can be seen that during the summer monsoon (JJAS), generally more rainfall is projected in the northern part of the region (approximately from 20°N northward), whereas drier conditions are projected for the Maritime Continent. Northeast Winter Monsoon The scale of projected precipitation changes (e.g. increases over land) for extremes during DJF is not as significant as the changes seen in the SW summer monsoon. Extreme rainfall indices In general, Rx1day and Rx5day for the end-century are projected to increase in areas north of the 15°N latitude. Extreme temperature indices In general, a 1-3°C change is projected for most land regions of Southeast Asia across all RCM projections for the mid-century and 3-5°C change for the end-century. Selected examples/figures from main report on following slides © Crown copyright Met Office Projected Changes – Annual temperature Summary of estimated mid-term and long-term projections of annual cycle temperature changes. © Crown copyright Met Office Seasonal Mean temperature © Crown copyright Future changes in seasonal mean temperature (°C) for end-century (20712100) relative to the baseline period (1971-2000) in JJA for ECHAM5 and Met Office HadCM3Q0, 3, 10, 11, 13 for the A1B scenario. Seasonal rainfall change © Crown copyright The median values (from the 6 simulations) of the changes of the late century seasonal rainfall. The areas where all the 6 simulations agree on the change sign are hatched. Met Office Southwest Summer Monsoon Changes End-century changes of average 850 hPa wind (vectors) and rainfall (scalar) for June, July, August and September (left to right columns) compared to the baseline period (1971-2000). From top to bottom are the different model projections, HadCM3Q10, 11, 13, 0, 3 and ECHAM5. Purple (green) shades indicate © Crown(decrease) copyright Met in Office increase rainfall intensity during that month. Changes in Annual Mean Maximum Temperature (Tmax) Projected changes in annual mean TXx (annual maximum day time temperature in °C), from HadCM3Q0, 3, 10, 11, 13 and ECHAM5 for mid-century (top row) and end-century (bottom row). © Crown copyright Met Office Key findings - Summary • Annual cycle change in temperature for the mid-term projections ranged around 2°C, and for the long-term around 4°C. These were statistically significant projections with fairly consistent changes registered across the year. • In contrast to temperature projections, the projections for precipitation showed a lot more variations across countries and seasons which lead to difficulties in interpretation of the annual cycle plots. • Unlike for temperature, changes in rainfall projections show large spatial and seasonal variations. Generally, the projections show drier climate over the sea and wetter climate over land. • The land-sea contrast is more obvious towards the end of the century. In all of the HadCM3Q projections, drier climate is projected over most areas during boreal winter except central mainland Southeast Asia. • However, wetter climate is projected south of the equator in ECHAM5. Generally, intermodel agreement is high except during winter (DJF). • For extreme rainfall indices, Rx1day and Rx5day for the end-century are projected to increase in areas north of the 15°N latitude. © Crown copyright Met Office Key findings - Summary • All projections show an increase in Consecutive Dry Days (i.e. longer dry spells) south of 15°N latitude in both time periods. For these projections, model agreement tends to be good. • For extreme temperature indices, a 1-3°C change is projected for most land regions of Southeast Asia across all RCM projections for the mid-century and a 3-5°C change for the end of the century. • The magnitudes of change for these two time periods are comparable across all four indices (TXx, TNx, TMx, and TMn) considered. © Crown copyright Met Office Summary and next steps © Crown copyright Met Office Key findings - Benefits • This project allowed national scientists to develop detailed climate scenarios for ASEAN countries, further enabling them to understand and communicate the implications of climate change within their country. • It has delivered scientifically sound information, relevant to raising awareness and understanding of climate change, developing national adaptation and mitigation plans. • Therefore enabling further building the capability of ASEAN countries to engage more effectively in international climate change forums. • The project has already started to strengthen the ability of the ASEAN countries to develop national adaptation and mitigation plans based on sound science and to engage with more confidence and a better information base in the UNFCCC, ASEAN and related forums. • This aims to increase the number of countries that reflect climate change mainstreaming in budgets, plans, profiles and national policy. For example, the implications for national policy on agriculture and food security, forestry, land use change, disaster risk reduction, health and water resources. © Crown copyright Met Office Next steps • We now have an established network of partners to develop capability going forward. • The DURIAN experiments generally perform well when compared to historic rainfall and temperature data. • A number of information gaps and potential improvements have been identified and it is proposed that further work is undertaken to address these. • A resourced programme of activity should be identified and partners should work together to support ongoing development, maintenance and validation. • The PRECIS model has a number of potential applications and can be used to inform risk and vulnerability assessments and subsequent adaptation strategies for a number of areas (for example agriculture, health, water management, disaster risk reduction and early warning systems, transport etc). • The SEACAM model should be utilised to inform and underpin regional strategies across these areas where appropriate © Crown copyright Met Office Further information www.precisrcm.com/rcct © Crown copyright Met Office For further information please contact: Jane Sattary, International Development Manager Met Office [email protected] © Crown copyright Met Office