Results of Climate Modeling: Applications and Case Studies Global Environmental Change: Changes in Climate System: Observation and Modeling of Climate Change

Procedures for constructing climate scenarios for use in impact assessments (Mearns et al. 2003).

As summarised in the IPCC TAR (Chapter 13, WGI), there are five key sources of uncertainties associated with constructing climate scenarios for impact and adaptation assessments: Uncertainties in future emissions of greenhouse gases (GHGs); Uncertainties in converting emissions to GHG concentrations; Uncertainties in converting concentrations to radiative forcing; Uncertainties in modelling climate response to a given forcing; Uncertainties in converting model response into inputs for impact studies.

Typology of climate extremes Hazards are not the same as extreme events, though they are related. Hazards are events and combination of events with a propensity to cause harm, whereas extreme events are defined through rarity, impact, or a combination of both (e.g. some more common events have extreme impacts, as in hurricane or tropical cyclone, referred to as extreme events because of the damage they cause, rather than through rarity. From Jones et al (2004) based on Schneider and Sarukhan, 2001)

Assessing current climate risks Natural Hazard-based approach Vulnerability-based approach Are the climate hazards (including likelihood of occurrence) well understood? Are vulnerabilities of the system well understood? Assessing current climate risks Is the relationship between climate and impact well understood? Y Assess vulnerability and/or decide criteria for risk assessment (+ critical threshold) Y Collect socio-economic baseline and assess vulnerability to climate N Collect historical data, analyze and assess likelihood N Is the relationship between impacts and vulnerability well understood? Y Construct impact relationship/models and assess likelihood impact N Is the relationship b/w vulnerability & climate impact well understood? Is the relationship b/w vulnerability & climate hazards well understood? Y Construct impact relationship/models Characterized climate hazards, analyze and assess likelihood N Assess current vulnerability Y Assess vulnerability and/or decide criteria for risk assessment (+ critical threshold) N Adaptation analysis Assess future climate risks From Jones and Boer (2004)

Assessing Future Climate Risk Assessing future climate risk is quite complex since the uncertainty associated with future condition on climate is high. The sensitivity of system to climate change may not well understood due to unknown future socio-economic condition and unclear plan of adaptation for the future What adaptation planning horizon being developed Change in vulnerability No change in vulnerability Change in vulnerability Example

Assessing future climate risks Construct climate scenario Construct socio-economic scenario Assessing future climate risks Link scenarios to impact models Without adaptation With adaptation Conduct risk assessment Natural hazards-based assessment Vulnerability-based assessment Analyze risk (likelihood of climate-driven outcome) Analyze risk (likelihood of threshold exceedence) Prioritized Adaptations Test Adaptations Modified from Jones and Boer (2004)

Future climate scenarios: example on onset and characteristic of season

Projected Climate Change: Example for rainfall pattern How we can assess future climate variability under (climate change) elevated GHGs concentration? Current Emission projection scenarios? General Circulation Models? Future Rainfall Aug Dec May

Change in temperature (oC) Change in rainfall (%) 2020 2050 2080 dT & dCH under SRESA2 and SRESB2 in Indonesia using 5 GCMs (Boer dan Faqih, 2004) EVERY MODEL GIVES DIFFERENT OUTPUTS UNDER SIMILAR EMISSION SCENARIOS

Selecting Models Select GCM models that work well in your regions Use assemble models (a group of GCM models) for projecting future climates NOT rely on just one model As GCM gives low resolution of outputs, do downscaled GCM outputs for impact assessment

Example: Assessing current and future climate risk Assessing the relationship between current and future climate variability on crop productivity Assessing impact of extreme climate events and sea level rise on crop failure/damages Scenario analysis for the expansion of agriculture area Analysis of climate change impact on food security Cost-Benefit analysis of adaptation options

Crop calendar and plans for the expansion of agriculture area Assessing the relationship between current and future climate variability on crop productivity Soils Genetic Climate Observation Scenarios Crop calendar and plans for the expansion of agriculture area Crop Model Simulation Spatial and temporal yield variability under different climate, crop managements and genetic scenarios Spatial and temporal variability of crop production under different climate, crop managements and genetic scenarios

Assessing impact of extreme climate events and sea level rise on crop failure/damages Historical data on flood and drought Historical rainfall and stream flow data Flood/drought causing harm Climate change scenarios Rainfall/stream flow threshold value causing flood/drought Change in socio-economic conditions Change in frequency of the threshold Change in level of climate risk on crop production

The need for regionalization

Permasalahan dengan GCM Resolusi terlalu rendah (350x350 km) sementara untuk kajian dampak diperlukan resolusi yang lebih tinggi Oleh karena itu berkembanglah metode downscaling, yaitu merincikan resolusi GCM. Secara umum metode ini didasarkan pada pemadanan sebaran parameter klimatologi pada skala besar dan pada skala regional dengan menganggap adanya hubungan fungsional diantara ke dua skala tersebut. Penggunaan interpolasi untuk tujuan ini tidak disarankan karena akan misleading khususnya apabila wilayah memiliki terrain yang tidak homogen, dan ini merupakan kondisi umum yang dijumpai di Indonesia

Land masks of several CMIP3 GCMs defined by using land fraction data (IPCC terminology for this parameter is ‘SFTLF’) (Faqih 2010).

Metode downscaling Metode statistik: dengan membangun hubungan statistik antara peubah global dan peubah lokal. Misalnya hubungan antara SST dengan keragaman hujan di stasiun. Dengan menggunakan persamaan tersebut, iklim lokal dapat diprediksi dari data global. Persyaratan metode ini diantaranya memerlukan data historis yang panjang agar diperoleh model yang cukup robust (layak) Pada kondisi iklim yang berubah (studi tentang kajian perubahan iklim misalnya), hubungan statistik ini mungkin bisa tidak berlaku lagi.

Metode downscaling Metode Dinamik: pendekatan ini menggunakan model fisika yang komprehensif dari sistem iklim. Jadi proses-proses fisik yang terjadi di atmosfer dimodelkan mengikuti sifat iklim yang ada di wilayah dimaksud. Ada dua pendekatan Pertama model GCM dijalankan untuk satu periode (satu dekade) pada beberapa titik di permukaan laut berdasarkan kondisi suhu muka laut dan konsentrasi es pada titik tersebut dengan tingkat resolusi yang lebih tinggi dimana kondisi pada batas-batas (boundary condition) wilayah diambil dari percobaan-percobaan GCM. Regional Climate Models (RCMs). Model ini dijalankan berdasarkan data suhu muka laut dan es-laut dan kondisi pada semua batas-batas wilayah atmosfer (misalnya angin dan suhu). Resolusi dapat mencapai 50 km bahkan dapat mencapai resolusi 15 km.

Metode downscaling Metode statistik/dynamik: menghubungan simulasi model regional dan global melalui statistik yang diturunkan dari unsur cuaca skala global (misalnya agin geostropik). Ada dua cara: Pertama: Model RCM dijalankan berdasarkan kondisi batas hasil observasi pada wilayah tertentu pada berbagai kondisi cuaca tingkat skala global dan kemudian model GCM dijalankan pula pada kondisi cuaca yang sama dan dan dibangun hubungannya. Kemudian kondisi iklim di permukaan pada wilayah lain diperoleh lagi dengan menjalankan model RCM dengan menggunakan kondisi batas dari model GCM Kedua: sama dengan pertama tetapi model RCM dan GCM dijalankan dengan menggunakan kondisi batas GCM dan dibuat hubungan statistik kedua luaran. Kemudian model GCM dijalankan pada kondisi batas yang ditetapkan tanpa menjalankan RCM (Kondisi iklim pada tingkat resolusi tinggi diperoleh dengan menggunakan persamaan hubungan tersebut).

Regional Climate Predictions Climate model predictions need to be generated at finer scales than current models. Therefore, regional climate predictions is required. There are three different techniques: (i) statistical analysis – modelled output statistics (MOS); (ii) embedded high-resolution ‘regional climate model’ (RegCM), and (iii) timeslicing (high resolution global atmospheric model forced by SST results from a lower-resolution coupled model for a specific time period) are all employed to derived higher-resolution results from low-resolution model simulations for application to regional climate simulation

Regional Climate Model (RCM) Year 2 Regional Climate Model (RCM) The regional climate model (RCM) is one of the sophisticated downscaling techniques that physically based and represent most or all of the processes, interactions and feedbacks between the climate system components that is represented in GCM Considerations on regional characteristics as forcing factor on surface climate system, such as topography, lakes, the coastline, and land use distributions (Fu et al. 2005) The increases of resolution along with more accurate topography performed strong influences on model physics and dynamic that produce better simulation result over a particular region (Gao et al. 2006)

Why do we need regionalization? Comparison between observed data and regional climate model output; a) CRU TS2.1 observation, b) CMAP observation, and c) RegCM3 output 55 km x 55 km resolution

Thanks