EUCLIPSE First Period Report

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emote effects. In Figure 21 we propose a framework to identify the ideal diagnostics window for transpose-AMIP experiments. On the one hand, the lead-time should be long enough that the initialization shock has decayed sufficiently for the true physics errors (or physics differences) to dominate. On the other hand, the lead-time should be short enough that ‘noise’ associated with chaotic interactions with the resolved flow do not necessitate an unfeasibly large sample size. To test this framework, two forecast models and two sets of climate-resolution analyses are required. Here, T159 analyses using 4D Var data assimilation with a 6hour window have been made for two models for the month of April 2011. The first model is the control version of ECMWF’s integrated forecasting system (IFS). The second model differs from this in that it has a reduced entrainment rate for deep convection. Climate-resolution weather forecasts using each model are then initiated from each set of analyses (2x2=4 sets of forecasts started 4 times a day for 30 days). Figure 22 shows first results for the zonal mean forecast temperature differences following the approach proposed in Figure 21. The left-hand column shows mean forecast differences between the two models initiated from the same (control) analysis (corresponding to the blue and red models started from the blue analysis in Figure 21). The forecast differences are strongest in the convection dominated tropics during the first few forecast days while, at longer lead times, differences in the extra tropics start to dominate. The right-hand column of Figure 22 shows the mean forecast differences when the control model is initialized from the control analysis and from the analysis obtained using the perturbed model version (i.e. blue model started from the blue and red analyses in Figure 21). Forecast differences due to differences in the initial conditions (right-hand column) are larger than the differences due to model differences (left-hand column) for the first forecast days. The appropriate lead-time window for Transpose AMIP experiments will depend on the variable, the degree of inconsistency between the analysis and the forecast model, and the process of interest. In some cases, e.g. when the influence of the initialization becomes too large and errors are strongly dominated even after several forecast days by the initial shock, such a window might not exist, and the transpose-AMIP diagnostics might be uninformative. In such a case, the initial tendency approach can give deeper insights into errors of parameterizations of fast physical processes. A big advantage is that departures from the analysis for different variables can be split into contributions of different processes. Figure 23 shows the zonal mean temperature analysis increments for the two model versions, the difference in the increments, and the difference in temperature tendency from the convection parameterization over the six hour forecast period. The largest part of the difference in the assimilation increments can be explained by differences in the temperature tendencies coming from the deep convection. In this case the larger error of in the perturbed model could be identified directly. Both approaches will be analysed further and the feasibility for their utilization in the <strong>EUCLIPSE</strong> project will be assessed. Once a suitable test case is identified, results will be compared between the cooperating climate modelling centres. 42
Figure 21: Schematic framework to determine the optimal time window for transpose-AMIP climate model evaluation. 43
Page 1 and 2: PROJECT PERIODIC REPORT Grant Agree
Page 3 and 4: 3.1 Publishable summary Objectives:
Page 5 and 6: WP3 aims to evaluate how large-scal
Page 7 and 8: 3.2 Core of the report for the peri
Page 9 and 10: instantaneous COSP inputs have to b
Page 11 and 12: Figure 2. COSP outputs from one Jul
Page 13 and 14: Compositing methods - Climatology o
Page 15 and 16: Experiment Name and Description Exp
Page 17 and 18: Figure 3. Comparison of radar refle
Page 19 and 20: Figure 4. Evaluation and analysis o
Page 21 and 22: uncertainty, to investigate the dep
Page 23 and 24: perturbed future climate conditions
Page 25 and 26: Figure 8. Initial profiles of the t
Page 27 and 28: Figure 10. Inversion jumps of the l
Page 29 and 30: Investigator Affiliation Model ASTE
Page 31 and 32: Figure 13. Time series of the inver
Page 33 and 34: D3.4 and D3.5 Equilibrium Solutions
Page 35 and 36: i) Synthesis of ground-based atmosp
Page 37 and 38: Figure 18 : time series of incoming
Page 39 and 40: Figure 20. an example of simulated
Page 41: WP4 : Sensitivity Experiments and H
Page 45 and 46: Figure. 23. Zonal mean temperature
Page 47 and 48: school at this prestigious location
Page 49 and 50: D3.4 Identification and comparison
Page 51 and 52: List of acronyms AA - Academy of A
Page 53 and 54: MISR - Multi-angle Imaging SpectroR
Page 55 and 56: Del. no. Deliverable name Version W
Page 57: Milestones Please complete this tab

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