# REGIONAL CLIMATE AND DOWNSCALING

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1 REGIONAL CLIMATE AND DOWNSCALING Regional Climate Modelling at the Hungarian Meteorological Service ANDRÁS HORÁNYI (horanyi( Special thanks: : Gabriella Csima,, Péter Szabó, Gabriella Szépszó Hungarian Meteorological Service Numerical Modelling and Climate Dynamics Division 1

2 PREFACE REGIONAL CLIMATE MODELLING One objective of the Summer School: to bridge the gap between climate dynamists and climate modellers This talk: the gap will be demonstrated, since basically no theoretical aspects will be considered,, just practical questions, issues These issues are of interest for us in Hungary and we hope to learn a bit about them during the Summer School I don t pretend that I know more about this subject than any of you let s s discuss the raised issues TOGETHER (i.e. your active participation is welcome)! 2

3 TABLE OF CONTENTS The climate system and its modelling REGIONAL CLIMATE MODELLING Basic tools for regional climate modelling Open problems for regional climate modelling How the past climate looks like? Global vs. regional models Accounting for model errors The role of lateral boundary conditions Impact of resolution and domain size Significance of the changes Use of model information to impact studies Sensitivity to model formulation, LBCs and scenarios Ensemble (probabilistic) evaluation (RCM simulations at the Hungarian Meteorological Service) Summary, outlook 3

4 4

5 THE CLIMATE SYSTEM REGIONAL CLIMATE MODELLING 5

6 CLIMATE SIMULATIONS REGIONAL CLIMATE MODELLING The simulation of the climate is possible from the principle point of view: the components of the climate system more or less known, therefore they can be simulated and the relations between those components can be quantified tools available from numerical weather prediction It should be emphasised: the climate (the atmosphere) is a complex, non-linear system, therefore its behaviour cannot be anticipated speculatively the only available tool is modelling 6

7 THE MOST IMPORTANT COMPONENTS OF THE CLIMATE MODELS Land surface (soil) model Ocean models (sea-currents, sea ice) Atmospheric models Air chemistry (aerosols, carbon cycle) Biosphere 7

8 BASIS OF NUMERICAL WEATHER PREDICTION (1) The atmosphere (and the different elements of the climate system) can be described via its hydro-thermodynamic system of equations (physical laws in mathematical form) This system of partial differential equations can be solved numerically (there is no analytical solution) with the help of mathematical models Initial and boundary conditions (lower, upper, lateral) are needed 8

9 BASIS OF NUMERICAL WEATHER PREDICTION (2) Simplifications In the continuous equations: the height of the atmosphere is neglected with respect to the radius of the Earth spherical assumption hydrostatic assumption etc. In the solution: Discretisation Numerical schemes 9

10 HORIZONTAL AND VERTICAL DISCRETISATION (MODEL GRID) REGIONAL CLIMATE MODELLING 10

11 WHAT SIMULATIONS (PREDICTIONS) CAN BE EXPECTED BY CLIMATE MODELS? The average behaviour of the atmosphere (characterised statistically: mean values, cumulative quantities, standard deviation; anomaly from a reference etc.) The uncertainty of the simulation SHOULD BE also quantified (probabilistic form, ensemble technique) Note: a climate model might provide perfect climate simulation in spite of the fact that in between all the individual weather predictions were wrong 11

12 CLIMATE MODELS: BASIC TYPES Global Coupled Atmospheric-Ocean General Circulation Model (AOGCM) General Circulation Model (GCM) Regional limited area Climate Models (RCM) 12

13 GLOBAL CLIMATE MODELS REGIONAL CLIMATE MODELLING The climate projec ojections are performed over the entire globe (typical resolution: few 100 km) The possibility of the consideration of changing external constraints and forcings (e.g. the increase of CO 2 emission scenarios) The results are valid on GLOBAL average the regional trends CANNOT be assessed from the global simulations (the direction of the regional changes might be just the opposite than the global ones) 13

14 CO 2 EMISSION SCENARIOS REGIONAL CLIMATE MODELLING 14

15 REGIONAL CLIMATE MODELLING RESOLUTION INCREASE OF THE GENERAL CIRCULATION MODELS IPCC, AR4 15

16 SOME ISSUES REGIONAL CLIMATE MODELLING Uncertainties in the external forcings (scenarios) Natural variability (role of initial conditions) Integrations with different initial conditions 16 Klaus Keuler, 2009

17 17

18 THE SIMULATION OF REGIONAL CLIMATE Basis: the global climate models provide fairly good quality predictions on global average (without good global models there is no chance to have reliable regional simulations) High resolution or variable resolution global models (need for huge computer capacity, no lateral boundary problem) Regional climate models: limited are numerical weather prediction models (lateral boundary condition problem, lateral boundaries from the global models) Statistical tical downscaling: : establishment of statistical tical relationship between the global and regional climate (based on and computed from the past and considered as valid for the future) 18

19 NESTED (EMBEDDED) LIMITED AREA MODELS: MODEL CASCADE REGIONAL CLIMATE MODELLING GLOBAL MODEL LOCAL MODEL REGIONAL MODEL 19

20 VERIFICATION (TESTING) OF CLIMATE MODELS The simulation of past climate: application of global and regional databases Climatic Research Unit (CRU, Norwich, uea.ac..ac.uk) Re-analyses data (ERA40 or NCEP) National meteorological databases Based on the verification results: continuous further development of the climate models 20

21 VERIFICATION OF CLIMATE MODELS 21

22 ISSUES TO BE DISCUSSED 1. What we know about the past climate (near past)? 2. Global vs. regional models 3. Impact of lateral boundary conditions 4. Impact of resolution and domain size REGIONAL CLIMATE MODELLING 5. Model biases: dependence on the simulated values, dependance on the simulation periods 6. Use of model information to impact studies 7. Significance of the changes 8. Sensitivities to scenarios and LBCs 9. Ensemble (probabilistic) evaluation 22

23 23

24 QUESTIONS Do we know sufficiently well the (near) past climate? What is the best description of the past climate? What are the best databases for the description of the past climate? ERA40 (1,, re-analyses)? CRU (0,5 or 10, Climatic Research h Unit)? ECA (0,25,, European Climate Assessment)? National observational data (observational and gridded data: 0,1 for Hungary)? 24

25 TEMPERATURE ( , winter) 25

26 PRECIPITATION DIFFERENCE ( , winter) 26

27 ANSWERS TO ISSUE NO. 1 REGIONAL CLIMATE MODELLING We don t even know precisely how the past climate looked like ( the past is also uncertain ) The various databases might be rather different (especially near to orography and coastlines) One has to be careful while evaluating the climate models with respect to the observational databases (The ECA database seems to be the most appropriate) 27

28 28

29 DYNAMICAL DOWNSCALING REGIONAL CLIMATE MODELLING Regional model Global field Downscaling 29

30 QUESTIONS REGIONAL CLIMATE MODELLING Does the regional (limited area) model bring added value (additional details) to the global simulations (in other words: does it make sense to run regional climate models)? In principle: yes; in practise?? How to objectively measure the potential added value (bias, RMSE)? Can RCMs simulate an own circulation? 30

31 IN PRINCIPLE: YES (SEE OROGRAPHY: ECHAM VS. REMO; ARPEGE VS. ALADIN-CLIMATE) 31

32 GLOBAL (ECHAM) VS. REGIONAL (REMO) 32

33 ANNUAL TEMPERATURE and PRECIPITATION BIAS GLOBAL LAM 33

34 SEASONAL TEMPERATURE and PRECIPITATION BIAS 34

35 ANSWERS TO ISSUE NO. 2 REGIONAL CLIMATE MODELLING RCMs can improve regional details with the more precise description of the surface characteristics and with their higher horizontal resolution (possible own circulation) However simple statistical scores sometimes don t show real improvements One has to be careful since the traditional verification characteristics might penalise the higher resolution model 35

36 36

37 QUESTIONS REGIONAL CLIMATE MODELLING How can we treat the model errors, which are causing deficiencies in the model simulations in the past? How can the model error information based on past simulations be applied to improve the simulations for the future? Are the model errors temporally unchanged? Can we eliminate model errors? Are perfect models for the past perfect also for the future? Are bad (biased) models for the past useless to be used for the future? 37

38 THE EXISTENCE OF MODEL ERRORS 38

39 MODEL ERRORS: AN EXAMPLE (BIAS WITH RESPECT TO CRU, TEMPERATURE and PRECIPITATION, ALADIN-Climate) 39

40 DEPENDENCE OF MODEL ERRORS FROM THE SIMULATED VALUES (REMO) LBC: ERA40 40

41 TEMPORAL BIAS BEHAVIOUR LBC: ERA-40, temperature biases [ o C] HU: REGIONAL CLIMATE MODELLING Summer School, 2.0 Visegrád,

42 ANSWERS TO ISSUE NO. 3 REGIONAL CLIMATE MODELLING There are important biases of the models, which should be accounted for (BUT how?) The model errors are depending on the season, on the simulated values and also on the period (therefore it is not easy to use past model errors to de-bias biase future simulations) In practise: differences are taken between past and future model simulations (does anybody know something MUCH better?) 42

43 43

44 QUESTIONS REGIONAL CLIMATE MODELLING What is the role of the lateral boundary conditions (in principle they are the main driving factors)? What is more important? Perfect boundaries (re-analyses)? Dynamically (and physically) consistent boundaries? How to treat the sometimes ambiguous information obtained from the integrations for the past (based on perfect boundaries and global model s boundaries) 44

45 LBC: ERA40 REGIONAL CLIMATE MODELLING ERA40 VS. GLOBAL LBCs ( , 1990, TEMPERATURE BIAS) LBC: ECHAM 45

46 Temperature [ o C] REGIONAL CLIMATE MODELLING RELATION BETWEEN GLOBAL LBC-s s AND REGIONAL RESULTS LBC: ERA40 LBC: ECHAM 46

47 ANSWERS TO ISSUE NO. 4 REGIONAL CLIMATE MODELLING Our experiments show that the dynamical consistency is more important than the perfectness perfectness (the errors are significantly different for re-analyses and global driving favouring the global LBCs) The ERA40-driven runs are used to improve the model formulation (especially the physics) On the other hand the global model driven simulations are used for the determination of the climate change signals 47

48 48

49 QUESTIONS Are higher resolution simulations better than the coarser ones? Is the precise position of the applied domain important? 49

50 MODEL ERRORS (TEMPERATURE and PRECIPITATION, ALADIN-Climate) TEMPERATURE PRECIPITATION 10 km 10 km 25 km 25 km 50

51 ANSWERS TO ISSUE NO. 5 REGIONAL CLIMATE MODELLING The increase of resolution might not improve the results (it is not a magic solution ) There are other factors controlling the quality of the outputs (LBCs( LBCs,, domain size etc.): : cancellation of errors? 51

52 52

53 QUESTIONS How significance tests are important for demonstrating the changes? 53

54 CHANGE OF ANNUAL TEMPERATURE (TARGET PERIOD VS ) 1990) REMO ALADIN o C 1.5 o C ALADIN All the changes are significant! 3.5 o C 54

55 CHANGE OF ANNUAL PRECIPITATION (TARGET PERIOD VS ) 1990) REMO ALADIN No significancy over Hungary! ALADIN Only 1.4 % of the Hungarian territory is significant 55

56 REMO REGIONAL CLIMATE MODELLING CHANGE OF SEASONAL PRECIPITATION (TARGET PERIOD VS ) 1990) ALADIN % 0.1 % 0.0 % 2.3 % ALADIN % 0.0 % 0.0 % 1.1 % Spring 0.2 % 92 % 0.0 % 0.0 % Summer Autumn Winter 56

57 CHANGES IN PRECIPITATION EXCEEDING 20 MM (ALADIN-Climate, vs ): 1990): WITH AND WITHOUT SIGNIFICANCE 57

58 ANSWERS TO ISSUE NO. 6 REGIONAL CLIMATE MODELLING Significance tests are essential, since otherwise the results can be easily misinterpreted. One has to be very careful to interpret non-significant changes 58

59 59

60 QUESTIONS Can the raw model outputs be used directly for impact studies? How to bridge the gap between the resolution of the models and the resolution of the required information (statistical and/or dynamical methods?) 60

61 HOW W THE OUTPUTS OF THE CLIMATE MODELS CAN BE USED IN PRACTISE? Model outputs: main climatic characteristics of the atmosphere on a 3D grid Special (statistical and dynamical) downscaling (post-processing) processing) Impact studies (based on objective methods) Final users: economy, society, health care, politics. 61

62 ANSWERS TO ISSUE NO. 7 For impact studies the direct RCM outputs mostly cannot be used Either a downscaling (improve the temporal and spatial information content) or a post-processing processing (compute such fields which are not model variables) is needed This further downscaling is a difficult job The dynamical downscaling methods are recommended 62

63 63

64 QUESTIONS What is more important : LBC, model type or scenario? 64

65 DEPENDENCE ON THE MODEL FORMULATION, LBCs AND THE SCENARIO (TEMPERATURE, ) 2050) 65

66 ANSWERS TO ISSUE NO. 8 REGIONAL CLIMATE MODELLING The LBC-s s are more important than model formulation and scenarios for the near future ( ) Later ( ) 2100) the scenarios are the main driving forces 66

67 67

68 QUESTIONS What is the best way to provide information to the users? How tot quantify uncertainties? 68

69 ENSEMBLE EVALUATION OF MORE MODEL S OUTPUT (PRECIPITATION) 69

70 PRECIPITATION (BASED ON PRUDENCE RESULTS) 0.1 mm/day % 8 % 14 % 11 % 10 % 20 mm/day % 36 % 4 % 61 % 66 % 70

71 HEAT WAVES (BASED ON PRUDENCE RESULTS) % 84 % 46 % 60 % 189 % 71

72 ANSWERS TO ISSUE NO. 9 REGIONAL CLIMATE MODELLING In principle: all the uncertainties of the simulations should be understood and then in practise quantified In practise: : the t easiest way is to consider the outputs uts of more RCMs 72

73 73

74 SOME INTRODUCTORY REMARKS The climate modelling activities in Hungary had been started for 5 years Two regional climate models are adapted, tested, developed and applied: ALADIN-Climate (international cooperation) and REMO (Max Planck Institute) Cooperation with ELTE (Eötvös( Loránd University) 74

75 MAIN CHARACTERISTICS OF THE REMO SIMULATIONS Integration period Lateral boundary conditions ERA ERA40 (125 km) TRANSIENT ECHAM5/MPI-OM (200 km) LBC coupling frequency Horizontal resolution Vertical levels 6 hours 0,22 ~ 25 km 20 Domain Scenario A1B 75

76 MAIN CHARACTERISTICS OF THE ALADIN- Climate SIMULATIONS ERA40 TIME SLICES Integration periods Lateral boundary conditions ERA40 (125 km) , 1990, , ARPEGE/OPA (~50 km) LBC coupling frequency Horizontal resolution Vertical levels 6 hours 25 km and 10km 37 Domain Scenario A1B 76

77 VALIDATION EXPERIMENTS: ERA40 LBC-s (ANNUAL TEMPERATURE BIAS) REMO, 25km +2 o C ALADIN, 25km 100% ALADIN, 10km -1.1 o C -1.5 o C 100% 100% 77

78 VALIDATION EXPERIMENTS: ERA40 LBC-s (ANNUAL PRECIPITATION BIAS) REMO, 25km -0,8 mm/m ALADIN, 25km +15,3 mm/m 48,5% ALADIN, 10km mm/m 100% 99.6% 78

79 VALIDATION EXPERIMENTS: GCM LBC-s (ANNUAL TEMPERATURE BIAS) REMO-ECHAM, 25km +0 o C ALADIN-ARPEGE, 10km -0,8 o C 29,1% 93,3% 79

80 VALIDATION EXPERIMENTS: GCM LBC-s (ANNUAL PRECIPITATION BIAS) REMO-ECHAM, 25km +7,7 mm/m ALADIN-ARPEGE, 10km +9,3 o C 79,8% 87,6% 80

81 VALIDATION: SUMMARY AND QUESTIONS ERA40 coupling: REMO: warm and dry ALADIN-Climate: cold and wet GCM coupling: REMO: perfect temperature and wet ALADIN-Climate: cold (but warm summer) and wet (but dry autumn) Question: How to interpret the contradicting behaviour of the experiments for the past (especially for the REMO model)? 81

82 CHANGE OF ANNUAL TEMPERATURE (TARGET PERIOD VS ) 1990) REMO ALADIN o C 1.5 o C ALADIN All the changes are significant! 3.5 o C 82

83 REMO REGIONAL CLIMATE MODELLING CHANGE OF SEASONAL TEMPERATURE (TARGET PERIOD VS ) 1990) ALADIN ALADIN Spring Summer Autumn Winter 83

84 CHANGE OF ANNUAL PRECIPITATION (TARGET PERIOD VS ) 1990) REMO ALADIN ALADIN

85 REMO REGIONAL CLIMATE MODELLING CHANGE OF SEASONAL PRECIPITATION (TARGET PERIOD VS ) 1990) ALADIN ALADIN Spring Summer Autumn Winter 85

86 MONTHLY MEAN TEMPERATURE AND PRECIPITATION OVER HUNGARY (ALADIN MODEL) 86

87 CHANGE OF MEAN PRECIPITATION OVER HUNGARY (reference( reference: mean of ; 1990; REMO model) Summer Winter 87

89 CHANGES IN EXTREMES: HEAVY PRECIPITATION EVENTS (RR>20 mm) ALADIN REMO ALADIN

91 SUMMARY There are more questions than answers around regional climate models Some questions were raised and some hints were provided Some results obtained in Hungary were briefly introduced 91

92 92

93 93

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