The sensitivity of the tropical circulation and Maritime Continent precipitation to climate model resolution

The dependence of the annual mean tropical precipitation on horizontal resolution is investigated in the atmospheric version of the Hadley Centre General Environment Model. Reducing the grid spacing from about 350 km to about 110 km improves the precipitation distribution in most of the tropics. In particular, characteristic dry biases over South and Southeast Asia including the Maritime Continent as well as wet biases over the western tropical oceans are reduced. The annual-mean precipitation bias is reduced by about one third over the Maritime Continent and the neighbouring ocean basins associated with it via the Walker circulation. Sensitivity experiments show that much of the improvement with resolution in the Maritime Continent region is due to the specification of better resolved surface boundary conditions (land fraction, soil and vegetation parameters) at the higher resolution. It is shown that in particular the formulation of the coastal tiling scheme may cause resolution sensitivity of the mean simulated climate. The improvement in the tropical mean precipitation in this region is not primarily associated with the better representation of orography at the higher resolution, nor with changes in the eddy transport of moisture. Sizeable sensitivity to changes in the surface fields may be one of the reasons for the large variation of the mean tropical precipitation distribution seen across climate models.     

Mechanisms of tropical precipitation biases in climate models

Climate Dynamics - We investigate the possible causes for inter-model spread in tropical zonal-mean precipitation pattern, which is divided into hemispherically symmetric and anti-symmetric modes...     

A stochastic model of SST for climate simulation experiments

 This study describes the implementation of a statistical method to simulate a multi-century sequence of global sea surface temperature (SST) fields. A multi-variable auto-regressive (AR) model is trained on the observed time series of SST from the data set compiled at the Hadley Centre (GISST 2.0). To reduce the dimensionality of the model, the stochastic process is in practice fitted to empirical orthogonal function (EOF) time coefficients of the SST series, retaining the first 14 EOFs. Selected lag cross-covariances among the EOF time series are retained, based on the structure of the cross-correlation matrix and lags up to 64 months are included. Though the resulting system is quite large (a 14-dimensional AR process, with 400 parameters to be determined) the calculation is possible and a stable process is obtained. The process can then be used to investigate some statistical properties of the SST data set and to generate synthetic SST data that could be used in very long numerical experiments with atmospheric or ocean models in which only the main features of the observed statistics of the SST must be retained. Results indicate that the synthetic SST data set seems to be of usable quality as boundary condition for the atmosphere or the ocean in climate experiments. Analysis of extreme events and extreme decades in the synthetic SST data confirms the exceptional character of the 1980s, but also provides circumstantial evidence that the 1980s were indeed within the limits of the statistics of the previously observed record. Received: 6 August 1996 / Accepted: 29 September 1997     

Modeling the tropical Pacific Ocean using a regional coupled climate model

A high-resolution tropical Pacific general circulation model (GCM) coupled to a global atmospheric GCM is described in this paper. The atmosphere component is the 5°×4°global general circulation model of the Institute of Atmospheric Physics (IAP) with 9 levels in the vertical direction. The ocean component with a horizontal resolution of 0.5°, is based on a low-resolution model (2°×1°in longitude-latitude).Simulations of the ocean component are first compared with its previous version. Results show that the enhanced ocean horizontal resolution allows an improved ocean state to be simulated; this involves (1) an apparent decrease in errors in the tropical Pacific cold tongue region, which exists in many ocean models,(2) more realistic large-scale flows, and (3) an improved ability to simulate the interannual variability and a reduced root mean square error (RMSE) in a long time integration. In coupling these component models, a monthly "linear-regression" method is employed to correct the model's exchanged flux between the sea and the atmosphere. A 100-year integration conducted with the coupled GCM (CGCM) shows the effectiveness of such a method in reducing climate drift. Results from years 70 to 100 are described.The model produces a reasonably realistic annual cycle of equatorial SST. The large SSTA is confined to the eastern equatorial Pacific with little propagation. Irregular warm and cold events alternate with a broad spectrum of periods between 24 and 50 months, which is very realistic. But the simulated variability is weaker than the observed and is also asymmetric in the sense of the amplitude of the warm and cold events.     

Comparison of two bias correction methods for precipitation simulated with a regional climate model

This study evaluates the performance of two bias correction techniques—power transformation and gamma distribution adjustment—for Eta regional climate model (RCM) precipitation simulations. For the gamma distribution adjustment, the number of dry days is not taken as a fixed parameter; rather, we propose a new methodology for handling dry days. We consider two cases: the first case is defined as having a greater number of simulated dry days than the observed number, and the second case is defined as the opposite. The present climate period was divided into calibration and validation sets. We evaluate the results of the two bias correction techniques using the Kolmogorov-Smirnov nonparametric test and the sum of the differences between the cumulative distribution curves. These tests show that both correction techniques were effective in reducing errors and consequently improving the reliability of the simulations. However, the gamma distribution correction method proved to be more efficient, particularly in reducing the error in the number of dry days.

     

A nonlinear stochastic low-order energy balance climate model

The effects of stochastic forcing on a one-dimensional, energy balance climate model are considered. A linear, stochastic model is reviewed in analogy with the Brownian motion problem from classical statistical mechanics. An analogous nonlinear model is studied and shows different behavior from the linear model. The source of the nonlinearity is the dynamical heat transport. The role of nonlinearity in coupling different temporal and spatial scales of the atmosphere is examined. The Fokker-Planck equation from statistical mechanics is used to obtain a time evolution equation for the probability density function for the climate, and the climatic potential function is calculated. Analytical solutions to the steady-state Fokker-Planck equation are obtained, while the time-dependent solution is obtained numerically. The spread of the energy produced by a stochastic forcing element is found to be characterized by movement mainly from smaller to larger scales. Forced and free variations of climate are also explicitly considered.     

Variability of tropical cyclones over the southwest Pacific Ocean using a high-resolution climate model

Summary Knowledge of the variability in tropical cyclone (TC) frequency and distribution is essential in determining the possible impact of natural or human-induced climate change. This variability can be investigated using the available TC data bases and by carrying out long-term climate model simulations for both past and future climates. A coupled ocean-atmosphere climate model (referred to here as the OU-CGCM) is described and applied with a higher resolution (50 km) nested domain in the southwest Pacific region. Six-member ensembles of simulations with the OU-CGCM have been run for 80 years, from 1970 to 2050. During the period 1970–2000, the OU-CGCM runs were compared with the observed TC data base. For the period 2000–2050, two ensembles of simulations were performed, one with constant greenhouse gas concentrations and the second with increasing greenhouse gases. The OU-CGCM simulated well the observed TC frequency and distribution in the southwest Pacific during the period 1970–2000. It also produced clear interannual and interdecadal TC variability in both the fixed and enhanced greenhouse gas simulations during the period 2000–2050. The variability in TC frequencies was associated with the typical atmospheric and SST anomaly patterns that occur in periods of quiet and active TC frequencies. The main findings from the enhanced greenhouse gas scenario for the period 2000–2050 are: no change in the mean decadal number of TCs relative to the control run, but a marked increase of about 15% in the mean decadal number of TCs in the most severe WMO categories 4 and 5; the likelihood of TCs during the next 50-year period that are more intense than ever previously experienced in the Australian region; a poleward extension of TC tracks; and a poleward shift of over 2 degrees of latitude in the TC genesis region.     

动力气候模式降水预测能力初步评估

利用1982-2002年陕西省94个气象观测站逐月降水实况资料、国家气候中心制作的月动力延伸预测模式集合预报500hPa高度场资料,降尺度内插出94个站点月降水预测值,采用预报评分法（P）和距平符号异同评分法对内插预测结果检验。结果表明：预报评分法（P）评价结果较距平符号异同评分法评价结果整体偏高,二者在评分趋势上基本相同,尤其体现在降水较少的月份。     

Application of physical scaling towards downscaling climate model precipitation data

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.     

A study on combining global and regional climate model results for generating climate scenarios of temperature and precipitation for the Netherlands

Climate scenarios for the Netherlands are constructed by combining information from global and regional climate models employing a simplified, conceptual framework of three sources (levels) of uncertainty impacting on predictions of the local climate. In this framework, the first level of uncertainty is determined by the global radiation balance, resulting in a range of the projected changes in the global mean temperature. On the regional (1,000–5,000 km) scale, the response of the atmospheric circulation determines the second important level of uncertainty. The third level of uncertainty, acting mainly on a local scale of 10 (and less) to 1,000 km, is related to the small-scale processes, like for example those acting in atmospheric convection, clouds and atmospheric meso-scale circulations—processes that play an important role in extreme events which are highly relevant for society. Global climate models (GCMs) are the main tools to quantify the first two levels of uncertainty, while high resolution regional climate models (RCMs) are more suitable to quantify the third level. Along these lines, results of an ensemble of RCMs, driven by only two GCM boundaries and therefore spanning only a rather narrow range in future climate predictions, are rescaled to obtain a broader uncertainty range. The rescaling is done by first disentangling the climate change response in the RCM simulations into a part related to the circulation, and a residual part which is related to the global temperature rise. Second, these responses are rescaled using the range of the predictions of global temperature change and circulation change from five GCMs. These GCMs have been selected on their ability to simulate the present-day circulation, in particular over Europe. For the seasonal means, the rescaled RCM results obey the range in the GCM ensemble using a high and low emission scenario. Thus, the rescaled RCM results are consistent with the GCM results for the means, while adding information on the small scales and the extremes. The method can be interpreted as a combined statistical–dynamical downscaling approach, with the statistical relations based on regional model output.     

An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model

Coupled atmosphere–ocean general circulation models (AOGCMs) commonly fail to simulate the eastern equatorial Atlantic boreal summer cold tongue and produce a westerly equatorial trade wind bias. This tropical Atlantic bias problem is investigated with a high-resolution (27-km atmosphere represented by the Weather Research and Forecasting Model, 9-km ocean represented by the Regional Ocean Modeling System) coupled regional climate model. Uncoupled atmospheric simulations test climate sensitivity to cumulus, land-surface, planetary boundary layer, microphysics, and radiation parameterizations and reveal that the radiation scheme has a pronounced impact in the tropical Atlantic. The CAM radiation simulates a dry precipitation (up to ?90%) and cold land-surface temperature (up to ?8?K) bias over the Amazon related to an over-representation of low-level clouds and almost basin-wide westerly trade wind bias. The Rapid Radiative Transfer Model and Goddard radiation simulates doubled Amazon and Congo Basin precipitation rates and a weak eastern Atlantic trade wind bias. Season-long high-resolution coupled regional model experiments indicate that the initiation of the warm eastern equatorial Atlantic sea surface temperature (SST) bias is more sensitive to the local rather than basin-wide trade wind bias and to a wet Congo Basin instead of dry Amazon—which differs from AOGCM simulations. Comparisons between coupled and uncoupled simulations suggest a regional Bjerknes feedback confined to the eastern equatorial Atlantic amplifies the initial SST, wind, and deepened thermocline bias, while barrier layer feedbacks are relatively unimportant. The SST bias in some CRCM simulations resembles the typical AOGCM bias indicating that increasing resolution is unlikely a simple solution to this problem.     

Changes in future precipitation over South Korea using a global high-resolution climate model

We used daily precipitation data from a global high-resolution climate scenario to analyze the features of future precipitation including extreme and heavy rainfall. The scenario shows that the model reproduces the daily precipitation over South Korea well. The projections show an increase in annual precipitation of approximately 18% in the late 21st century, with the highest increase (38%) occurring in winter. The number of days with daily precipitation of less than 5 mm decreases, but that of daily precipitation of more than 5 mm increases slightly in the latter part of the 21st century. The peak of precipitation days shifted from July to August. The number of days with relatively small amounts of precipitation (10 and 30 mm d^?1) increases most substantially in the winter season, but that for large amounts of precipitation (50, 80, 100, and 130 mm d^?1) increases most in the summer season. Events with heavy precipitation rates of 100 and 130 mm d^?1 are expected to occur in the winter season in the late 21st century, although no such events occurred during the winter season in the reference period.     

Influence of the oceanic biology on the tropical Pacific climate in a coupled general circulation model

The influence of chlorophyll spatial patterns and variability on the tropical Pacific climate is investigated by using a fully coupled general circulation model (HadOPA) coupled to a state-of-the-art biogeochemical model (PISCES). The simulated chlorophyll concentrations can feedback onto the ocean by modifying the vertical distribution of radiant heating. This fully interactive biological-ocean-atmosphere experiment is compared to a reference experiment that uses a constant chlorophyll concentration (0.06 mg m⁻³). It is shown that introducing an interactive biology acts to warm the surface eastern equatorial Pacific by about 0.5°C. Two competing processes are involved in generating this warming: (a) a direct 1-D biological warming process in the top layers (0–30 m) resulting from strong chlorophyll concentrations in the upwelling region and enhanced by positive dynamical feedbacks (weaker trade winds, surface currents and upwelling) and (b) a 2-D meridional cooling process which brings cold off-equatorial anomalies from the subsurface into the equatorial mixed layer through the meridional cells. Sensitivity experiments show that the climatological horizontal structure of the chlorophyll field in the upper layers is crucial to maintain the eastern Pacific warming. Concerning the variability, introducing an interactive biology slightly reduces the strength of the seasonal cycle, with stronger SST warming and chlorophyll concentrations during the upwelling season. In addition, ENSO amplitude is slightly increased. Similar experiments performed with another coupled general circulation model (IPSL-CM4) exhibit the same behaviour as in HadOPA, hence showing the robustness of the results.     

Fine-resolution regional climate model simulations of the impact of climate change on tropical cyclones near Australia

Fine-resolution regional climate simulations of tropical cyclones (TCs) are performed over the eastern Australian region. The horizontal resolution (30 km) is fine enough that a good climatological simulation of observed tropical cyclone formation is obtained using the observed tropical cyclone lower wind speed threshold (17 m s^–1). This simulation is performed without the insertion of artificial vortices (bogussing). The simulated occurrence of cyclones, measured in numbers of days of cyclone activity, is slightly greater than observed. While the model-simulated distribution of central pressures resembles that observed, simulated wind speeds are generally rather lower, due to weaker than observed pressure gradients close to the centres of the simulated storms. Simulations of the effect of climate change are performed. Under enhanced greenhouse conditions, simulated numbers of TCs do not change very much compared with those simulated for the current climate, nor do regions of occurrence. There is a 56% increase in the number of simulated storms with maximum winds greater than 30 m s^–1 (alternatively, a 26% increase in the number of storms with central pressures less than 970 hPa). In addition, there is an increase in the number of intense storms simulated south of 30°S. This increase in simulated maximum storm intensity is consistent with previous studies of the impact of climate change on tropical cyclone wind speeds.     

Comparison of precipitation in the regional climate model BALTIMOS to radar observations

Observational data and simulations of the regional climate system Baltic integrated model system (BALTIMOS) were used to study precipitation in the Baltic Sea and its drainage basin with a special focus on the diurnal cycle. The study includes a general evaluation of BALTIMOS precipitation, showing that BALTIMOS has too many light rain events causing an overestimation of the total annual precipitation amount. The diurnal cycle as well as its spatial distribution was analysed. BALTIMOS captures the broad characteristics: a significant diurnal variability with an afternoon peak above land and weak variability with a nocturnal peak above sea. An algorithm to distinguish between frontal and convective precipitation was applied to examine the diurnal cycle more thoroughly. The local solar time of maximum rain in summer is about 1 to 2 h earlier in BALTIMOS than in radar observations of precipitation.     

Sensitivity of tropical Atlantic climate to mixing in a coupled ocean–atmosphere model

The sensitivity of tropical Atlantic climate to upper ocean mixing is investigated using an ocean-only model and a coupled ocean–atmosphere model. The upper ocean thermal structure and associated atmospheric circulation prove to be strongly related to the strength of upper ocean mixing. Using the heat balance in the mixed layer it is shown that an excessively cold equatorial cold tongue can be attributed to entrainment flux at the base of the oceanic mixed layer, that is too large. Enhanced entrainment efficiency acts to deepen the mixed layer and causes strong reduction in the upper ocean divergence in the central equatorial Atlantic. As a result, the simulated sea surface temperature, thermocline structure, and upwelling velocities are close to the observed estimates. In the coupled model, the seasonal migration of the Intertropical Convergence Zone (ITCZ) reduces when the entrainment efficiency in the oceanic mixed layer is enhanced. The precipitation rates decrease in the equatorial region and increase along 10°N, resulting in a more realistic Atlantic Marine ITCZ. The reduced meridional surface temperature gradient in the eastern tropical Atlantic prohibits the development of convective precipitation in the southeastern part of the tropical Atlantic. Also, the simulation of tropical Atlantic variability as expressed in the meridional gradient mode and the eastern cold tongue mode improves when the entrainment efficiency is enhanced.     

Conditional stochastic simulation model for spatial downscaling for assessing the effects of climate change on hydro-meteorological variables

The current study examines the recently proposed “bias correction and stochastic analogues” (BCSA) statistical spatial downscaling technique and attempts to improve it by conditioning coarse resolution data when generating replicates. While the BCSA method reproduces the statistical features of the observed fine data, this existing model does not replicate the observed coarse spatial pattern, and subsequently, the cross-correlation between the observed coarse data and downscaled fine data with the model cannot be preserved. To address the dissimilarity between the BCSA downscaled data and observed fine data, a new statistical spatial downscaling method, “conditional stochastic simulation with bias correction” (BCCS), which employs the conditional multivariate distribution and principal component analysis, is proposed. Gridded observed climate data of mean daily precipitation (mm/day) covering a month at 1/8° for a fine resolution and at 1° for a coarse resolution over Florida for the current and future periods were used to verify and cross-validate the proposed technique. The observed coarse and fine data cover the 50-year period from 1950 to1999, and the future RCP4.5 and RCP8.5 climate scenarios cover the 100-year period from 2000 to 2099. The verification and cross-validation results show that the proposed BCCS downscaling method serves as an effective alternative means of downscaling monthly precipitation levels to assess climate change effects on hydrological variables. The RCP4.5 and RCP8.5 GCM scenarios are successfully downscaled.     

On the linear response of tropical African climate to SST changes deduced from regional climate model simulations

Summary Previous studies have highlighted the crucial role of sea surface temperature (SST) anomalies in the tropical Atlantic region in forcing the summer monsoon rainfall over subsaharan West Africa. Understanding the physical processes, relating SST variations to changes in the amount and distribution of African rainfall, is a key factor in improving weather and climate forecasts in this highly vulnerable region. Here, we present sensitivity experiments from a regional climate model with prescribed warmer tropical SSTs, according to enhanced greenhouse conditions at the end of the 21st century. This dynamical downscaling approach provides information about the nonlinear response of the atmosphere to oceanic heating. It has been suggested that the response is at least partly accounted for by the linear theory of tropical dynamics, involving a Kelvin and Rossby wave response to a tropical heat source. We compute the major modes of the linear Matsuno-Gill model for geopotential height and horizontal wind components and project the simulated response patterns onto these linear modes, in order to evaluate to which extent the simple linear theory may explain the SST-induced climate anomalies over Africa. A multivariate Hotelling T² test is used to evaluate whether these anomalies are statistically significant. Forcing the regional climate model by warmer SSTs leads to substantial climate anomalies over tropical Africa: Rainfall is increases over the Guinea Coast region (GCR) and tropical East Africa, but decreases over the Congo Basin and the Sahel Zone (SHZ). At the 850 hPa level, a trough develops over southern West Africa and the Gulf of Guinea, and is associated with stronger surface wind convergence over the GCR. These changes in the atmospheric dynamics strongly project onto the leading modes of the linear Matsuno-Gill model at various zonal wave numbers. The corresponding atmospheric heating pattern is highly reminiscent of the simulated nonlinear model reponse. The T² test statistics reveal that the SST forcing induces a statistically significant climate anomaly over tropical Africa if the climate state vector is reduced by projecting the simulated data onto the leading 10 linear modes. It is also shown that the linear response prevails in a long-term simulation with more realistic lower and lateral boundary conditions. Thus, linear tropical dynamics are assumed to be a major physical process on the ground of the prominent SST-African rainfall relationship.