ALBERT

Description: Influence of microphysical schemes on atmospheric water in the Weather Research and Forecasting model Geoscientific Model Development Discussions, 6, 4563-4601, 2013 Author(s): F. Cossu and K. Hocke This study examines how different microphysical parameterization schemes influence orographically-induced precipitation and the distributions of hydrometeors and water vapour for mid-latitude summer conditions in the Weather Research and Forecasting (WRF) model. A high-resolution two-dimensional idealized simulation is used to assess the differences between the schemes in which a moist air flow is interacting with a bell-shaped 2 km high mountain. Periodic lateral boundary conditions are chosen to recirculate atmospheric water in the domain. It is found that the 13 selected microphysical schemes conserve the water in the model domain. The gain or loss of water is less than 0.81% over a simulation time interval of 61 days. The differences of the microphysical schemes in terms of the distributions of water vapour, hydrometeors and accumulated precipitation are presented and discussed. The Kessler scheme, the only scheme without ice-phase processes, shows final values of cloud liquid water 14 times greater than the other schemes. The differences among the other schemes are not as extreme, but still they differ up to 79% in water vapour, up to 10 times in hydrometeors and up to 64% in accumulated precipitation at the end of the simulation. The microphysical schemes also differ in the surface evaporation rate. The WRF single-moment 3-class scheme has the highest surface evaporation rate compensated by the highest precipitation rate. The different distributions of hydrometeors and water vapour of the microphysical schemes induce differences up to 49 W m −2 in the downwelling shortwave radiation and up to 33 W m −2 in the downwelling longwave radiation.

Description: AnaWEGE: a weather generator based on analogues of atmospheric circulation Geoscientific Model Development Discussions, 6, 4745-4774, 2013 Author(s): P. Yiou This paper presents a stochastic weather generator based on analogues of circulation (AnaWEGE). Analogues of circulation have been a promising paradigm to analyse climate variability and its extremes. The weather generator uses precomputed analogues of sea-level pressure over the North Atlantic. The stochastic rules of the generator constrain the continuity in time of the simulations. The generator then simulates spatially coherent time series of a climate variable, drawn from meteorological observations. The weather generator is tested for European temperatures, and for winter and summer seasons. The biases in temperature quantiles and autocorrelation are rather small compared to observed variability. The ability of simulating extremely hot summers and cold winters is also assessed.

Description: Methodological aspects of a pattern-scaling approach to produce global fields of monthly means of daily maximum and minimum temperature Geoscientific Model Development Discussions, 6, 4833-4882, 2013 Author(s): S. Kremser, G. E. Bodeker, and J. Lewis A Climate Pattern-Scaling Model (CPSM) that simulates global patterns of climate change, for a prescribed emissions scenario, is described. A CPSM works by quantitatively establishing the statistical relationship between a climate variable at a specific location (e.g. daily maximum surface temperature, T max ) and one or more predictor time series (e.g. global mean surface temperature, T global ) – referred to as the "training" of the CPSM. This training uses a regression model to derive fit-coefficients that describe the statistical relationship between the predictor time series and the target climate variable time series. Once that relationship has been determined, and given the predictor time series for any greenhouse gas (GHG) emissions scenario, the change in the climate variable of interest can be reconstructed – referred to as the "application" of the CPSM. The advantage of using a CPSM rather than a typical atmosphere-ocean global climate model (AOGCM) is that the predictor time series required by the CPSM can usually be generated quickly using a simple climate model (SCM) for any prescribed GHG emissions scenario and then applied to generate global fields of the climate variable of interest. The training can be performed either on historical measurements or on output from an AOGCM. Using model output from 21st century simulations has the advantage that the climate change signal is more pronounced than in historical data and therefore a more robust statistical relationship is obtained. The disadvantage of using AOGCM output is that the CPSM training might be compromised by any AOGCM inadequacies. For the purposes of exploring the various methodological aspects of the CPSM approach, AOGCM output was used in this study to train the CPSM. These investigations of the CPSM methodology focus on monthly mean fields of daily temperature extremes ( T max and T min ). Key conclusions are: (1) overall, the CPSM trained on simulations based on the Representative Concentration Pathway (RCP) 8.5 emissions scenario is able to reproduce AOGCM simulations of T max and T min based on predictor time series from an RCP 4.5 emissions scenario; (2) access to hemisphere average land and ocean temperatures as predictors improves the variance that can be explained, particularly over the oceans; (3) regression model fit-coefficients derived from individual simulations based on the RCP 2.6, 4.5 and 8.5 emissions scenarios agree well over most regions of the globe (the Arctic is the exception); (4) training the CPSM on concatenated time series from an ensemble of simulations does not result in fit-coefficients that explain significantly more of the variance than an approach that weights results based on single simulation fits; and (5) the inclusion of a linear time dependence in the regression model fit-coefficients improves the variance explained, primarily over the oceans.

Description: Modeling different freeze/thaw processes in heterogeneous landscapes of the Arctic polygonal tundra using an ecosystem model Geoscientific Model Development Discussions, 6, 4883-4932, 2013 Author(s): given_name prefix surname suffix, S. Yi, K. Wischnewski, M. Langer, S. Muster, and J. Boike Freeze/thaw (F/T) processes can be quite different under the various land surface types found in the heterogeneous polygonal tundra of the Arctic. Proper simulation of these different processes is essential for accurate prediction of the release of greenhouse gases under a warming climate scenario. In this study we have modified the dynamic organic soil version of the Terrestrial Ecosystem Model (DOS-TEM) to simulate F/T processes beneath the polygon rims, polygon centers (with and without water), and lakes that are common features in Arctic lowland regions. We first verified the F/T algorithm in the DOS-TEM against analytical solutions, and then compared the results with in situ measurements from Samoylov Island, Siberia. In the final stage, we examined the different responses of the F/T processes for different water levels at the various land surface types. The simulations revealed that (1) the DOS-TEM was very efficient and its results compared very well with analytical solutions for idealized cases, (2) the simulations compared reasonably well with in situ measurements although there were a number of model limitations and uncertainties, (3) the DOS-TEM was able to successfully simulate the differences in F/T dynamics under different land surface types, and (4) permafrost beneath water bodies was found to respond highly sensitive to changes in water depths between 1 and 2 m. Our results indicate that water is very important in the thermal processes simulated by the DOS-TEM; the heterogeneous nature of the landscape and different water depths therefore need to be taken into account when simulating methane emission responses to a warming climate.

Description: A standard test case suite for two-dimensional linear transport on the sphere: results from a collection of state-of-the-art schemes Geoscientific Model Development Discussions, 6, 4983-5076, 2013 Author(s): P. H. Lauritzen, P. A. Ullrich, C. Jablonowski, P. A. Bosler, D. Calhoun, A. J. Conley, T. Enomoto, L. Dong, S. Dubey, O. Guba, A. B. Hansen, E. Kaas, J. Kent, J.-F. Lamarque, M. J. Prather, D. Reinert, V. V. Shashkin, W. C. Skamarock, B. Sørensen, M. A. Taylor, and M. A. Tolstykh Recently, a standard test case suite for 2-D linear transport on the sphere was proposed to assess important aspects of accuracy in geophysical fluid dynamics with a "minimal" set of idealized model configurations/runs/diagnostics. Here we present results from 19 state-of-the-art transport scheme formulations based on finite-difference/finite-volume methods as well as emerging (in the context of atmospheric/oceanographic sciences) Galerkin methods. Discretization grids range from traditional regular latitude-longitude grids to more isotropic domain discretizations such as icosahedral and cubed-sphere tessellations of the sphere. The schemes are evaluated using a wide range of diagnostics in idealized flow environments. Accuracy is assessed in single- and two-tracer configurations using conventional error norms as well as novel diagnostics designed for climate and climate-chemistry applications. In addition, algorithmic considerations that may be important for computational efficiency are reported on. The latter is inevitably computing platform dependent, The ensemble of results from a wide variety of schemes presented here helps shed light on the ability of the test case suite diagnostics and flow settings to discriminate between algorithms and provide insights into accuracy in the context of global atmospheric/ocean modeling. A library of benchmark results is provided to facilitate scheme intercomparison and model development. Simple software and data-sets are made available to facilitate the process of model evaluation and scheme intercomparison.

Description: On the analytic approximation of bulk collision rates of non-spherical hydrometeors Geoscientific Model Development Discussions, 6, 5077-5116, 2013 Author(s): A. Seifert, U. Blahak, and R. Buhr Analytic approximations of the binary collision rates of hydrometeors are derived for use in bulk microphysical parameterizations. Special attention is given to non-spherical hydrometeors like raindrops and snowflakes. The terminal fall velocity of these particles cannot be sufficiently well approximated by power law relations which are used in most microphysical parameterizations and therefore an improved formulation is needed. The analytic approximations of the bulk collision rates given in this paper are an alternative to look-up tables and can replace the Wisner approximation which is used in many atmospheric models.

Description: Ensemble initialization of the oceanic component of a coupled model through bred vectors at seasonal-to-interannual time scales Geoscientific Model Development Discussions, 6, 5189-5214, 2013 Author(s): J. Baehr and R. Piontek We evaluate the ensemble spread at seasonal-to-interannual time scales for two perturbation techniques implemented into the ocean component of a coupled model: (1) lagged initial conditions as commonly used for decadal predictions, (2) bred vectors as commonly used for weather and seasonal forecasting. We show that relative to an uninitialized reference simulation the implementation for bred vectors can improve the ensemble spread compared to lagged initialization at time scales from one months up to three years. As bred vectors have so far mostly been used at short time scales, we initially focus on the implementation of the bred vectors into the ocean component. We introduce a depth-dependent vertical rescaling norm, accounting for the vertical dependence of the variability, and extending the commonly used upper-ocean rescaling norm to the full water column. We further show that it is sufficient for the (sub-surface) ocean to breed temperature and salinity (i.e., scalar quantities), and rely on the governing physics to carry the temperature and salinity perturbations to the flow field. Using these bred vectors with a rescaling interval of 12 months, we initialize hindcast simulations and compare them to hindcast simulations initialized with lagged initial conditions. We quantify the ensemble spread by analyzing Talagrand diagrams and spread-error ratios. For both temperature and salinity, the lagged initialized ensemble is particularly under-dispersive for the first few months of predictable lead time. The ensemble initialized with bred vectors improves the spread for temperature and salinity for the 0–700 m and 1000–3500 m means, compared to the lagged ensemble at lead times of several months to one year. As the lead time increases to years, the differences between the two ensemble initialization techniques becomes more difficult to discern. While the results need to be confirmed in an initialized framework, the present analysis represents a first step towards an improved ensemble generation at the transition from seasonal-to-interannual time scales, in particular at lead times up to one year.

Description: Understanding the performance of the FLake model over the African Great Lakes Geoscientific Model Development Discussions, 6, 5141-5187, 2013 Author(s): W. Thiery, A. Martynov, F. Darchambeau, J.-P. Descy, P.-D. Plisnier, L. Sushama, and N. P. M. van Lipzig The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu, Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding Automatic Weather Stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration allowed to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and temperature structure. At Lake Kivu, mixolimnion temperatures predicted by FLake were found sensitive both to minimal variations in the external parameters (lake depth and water transparency) as to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may already lead to a regime switch from the correctly represented seasonal mixed layer deepening to either completely mixed (down to the model lake bottom) or permanently stratified (from ~10 m downwards) conditions. In contrast, model temperatures are found robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterize tropical lake water surface temperatures within atmospheric prediction models, but may be less appropriate, in its current form, to study complex limnological processes within tropical lakes. Furthermore, a study of different initial conditions showed that for tropical lakes lacking reliable initial data, a fully mixed, artificially warm initialisation is to be preferred, but only if the model is allowed to spin up until convergence is reached. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60 m during the main dry season is primarily due to enhanced lake evaporation and secondarily due to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.

Description: The mid-Pliocene climate simulated by FGOALS-g2 Geoscientific Model Development Discussions, 6, 2403-2428, 2013 Author(s): W. Zheng, Z. Zhang, L. Chen, and Y. Yu Within the framework of Pliocene Model Intercomparison Project (PlioMIP), the mid-Pliocene (3.264–3.025 Ma) climate simulated by the Flexible Global Ocean-Atmosphere-Land System model grid-point version 2 (FGOALS-g2) are analyzed in this study. Results show that the model reproduces the large-scale features of the global warming over the land and ocean. The simulated mid-Pliocene global annual mean surface air temperature (TAS) and sea surface temperature (SST) are 4.17 and 2.62°C warmer than the pre-Industrial simulation, respectively. In particular, the feature of larger warming over mid-high latitudes is well captured. In the simulated warm mid-Pliocene climate, the Atlantic Meridional Overturning Circulation (AMOC) and El Niño-Southern Oscillation (ENSO) become weaker.

Description: The potential of an observational data set for calibration of a computationally expensive computer model Geoscientific Model Development Discussions, 6, 2369-2401, 2013 Author(s): D. J. McNeall, P. G. Challenor, J. R. Gattiker, and E. J. Stone We measure the potential of an observational data set to constrain a set of inputs to a complex and computationally expensive computer model. We use each member in turn of an ensemble of output from a computationally expensive model, corresponding to some observable part of a modelled system, as a proxy for an observational data set. We argue that our ability to constrain uncertain parameter inputs to a model using its own output as data, provides a maximum bound for our ability to constrain the model inputs using observations of the real system. The ensemble provides a set of known parameter input and model output pairs, which we use to build a computationally efficient statistical proxy for the full computer model, termed an emulator. We use the emulator to find and rule out ''implausible" values for the inputs of held-out ensemble members, given the computer model output. As we know the true values of the inputs for the ensemble, we can compare our constraint of the model inputs with the true value of the input for any ensemble member. Measures of the quality of constraint have the potential to inform strategy for data collection campaigns, before any real-world data is collected, as well as acting as an effective sensitivity analysis. We use an ensemble of the ice sheet model Glimmer to demonstrate our measures of quality of constraint. The ensemble has 250 model runs with 5 uncertain input parameters, and an output variable representing the pattern of the thickness of ice over Greenland. We have an observation of historical ice sheet thickness that directly matches the output variable, and offers an opportunity to constrain the model. We show that different ways of summarising our output variable (ice volume, ice surface area and maximum ice thickness) offer different potential constraints on individual input parameters. We show that combining the observational data gives increased power to constrain the model. We investigate the impact of uncertainty in observations or in model biases on our measures, showing that even a modest uncertainty can seriously degrade the potential of the observational data to constrain the model.