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1.
Atmospheric general circulation models (AGCMs) are often “coupled” with time varying observations of boundary conditions
or some other aspect of the climate system. A typical example is the Atmospheric Model Intercomparison Project (AMIP) experimental
protocol, which required the specification of sea surface temperature and sea-ice extent from observed monthly means. AGCMs
ordinarily incorporate the prescribed conditions by evaluating an interpolating function at each time step. Typical schemes,
such as that used in the second generation GCM (GCM2) of the Canadian Centre for Climate Modelling and Analysis (CCC), do
not preserve monthly means and have a smoothing effect on the interpolated time series which tends to reduce the amplitude
of annual cycle and interannual variability of sea surface temperature (SST). By solving a large set of linear equations,
a simple linear time-interpolation scheme that preserves the observed monthly mean SST and hence its variability can be obtained.
The new scheme improves upon that used previously in CCC GCM2 by eliminating the substantial loss of interannual variability
(up to 20%) and the small attenuation of the annual cycle (less than 4% on average) incurred with the old scheme. The improved
linear interpolation scheme is easily adapted to other quantities.
Received: 4 August 1997 / Accepted: 26 November 1997 相似文献
2.
近50a中国降水格点数据集的建立及质量评估 总被引：11，自引：2，他引：9
基于2012年6月更新的高质量2 400个台站降水资料,采用薄盘样条法,制定了采用3个自变量(经度、纬度、海拔高度)、降水量开平方预处理、3次样条的插值方案,并引入数字高程资料,以减弱中国独特地形条件下高程对降水空间插值精度的影响,并对1961—2010年中国区域地面降水站点资料进行了空间内插,得到了中国地面降水0.5°×0.5°格点数据集。经数据集的质量评估结果表明:分析值与站点观测值均方根误差平均为0.49 mm,相关系数平均达0.93(通过0.01的显著性检验),夏季插值误差高于冬季,东南地区误差普遍高于其他地区。冬、春、夏、秋季绝大多数台站绝对误差在±10 mm/月以内。冬、春、夏、秋季分别有60%、82%、54%、77%的台站相对误差在±10%之间。插值后的格点化降水资料能够比较细致、准确地描述中国大陆年平均降水场的东南多、西北少的主要空间特征,但也平滑掉了范围很小的降水极值中心。台站分布越密集的地方,插值效果越好,并且最近距离小于40 km的台站插值精度较高,大于40 km插值精度衰减较快。 相似文献
3.
M. Fulakeza L. M. Druyan T. N. Krishnamurti 《Meteorology and Atmospheric Physics》2002,79(1-2):105-126
Summary
A regional climate model (RCM) is described which incorporates an improved scheme for soil moisture availability (SMA) compared
to an earlier version. The improvement introduces a sensitivity of SMA to soil type, vegetation cover and ground albedo, making
the model more adaptable to divers regions. In addition, the interactive SMA depends on past precipitation, ground temperature
and terrain relief. Six RCM simulations of the monthly mean climate over southern Africa are performed at 0.5° grid spacing.
Improvements in the RCM climate simulations compared to control runs are attributed to the newer SMA scheme. Only a slight
improvement in skill results from driving the RCM with observational analyses as opposed to GCM “predicted” lateral boundary
conditions. The high spatial resolution of the RCM provides a distinct advantage in the simulated spatial distribution of
precipitation compared with a global model run at an effective grid spacing of 2.8°. The mesoscale precipitation signal in
the RCM simulations is more dominant during the rather dry December 1982 than during December 1988. The improved SMA scheme
contributed to a realistic partition between latent and sensible heat fluxes at the ground-atmosphere boundary and consequently
a realistic diurnal cycle of ground temperature. Simulated differences in the spatial distribution of rainfall between December
1982 and December 1988 are more realistic with the improved scheme.
Received June 28, 2001 Revised August 27, 2001 相似文献
4.
Summary Six methods were used to interpolate the monthly mean climatological data from German climate stations to three Bavarian
forest climate stations. The observed forest climatological data at the Bavarian forest climate stations were used as the
reference data to which the interpolated data were compared. The results show that, for monthly mean daily maximum temperature
at valley and plain forest climate stations, each of the six interpolation methods can give accurate estimates. For monthly
mean daily maximum temperature, minimum temperature, air temperature and water vapor pressure at mountain forest climate stations,
topographically aided interpolation can give the most accurate estimates. Barnes interpolation combined with empirical transfer
functions can give accurate estimates forall climate variables at the plain and valley forest climate stations, and it can
also give accurate estimates for monthly mean wind speed and monthly precipitation at the mountain forest climate station.
The empirical transfer functions are very important for estimating the forest climatological data. These transfer functions
will be used for reconstruction of long-term forest climatological data in Bavaria.
Received September 9, 1998 Revised May 21, 1999 相似文献
5.
A temperate and boreal deforestation experiment has been performed at Météo-France using the ARPEGE climate model. A first
simulation was performed as a control with a present-day vegetation map, and another one with all forests north of 45 °N replaced
by meadows. Prescribed monthly mean climatological SSTs were used in both integrations. The ARPEGE climate model includes
a physically based land surface scheme, which has been tested both on snowfree and snow-covered sites, and has a relatively
high horizontal resolution. Results of the 4-year integrations suggest that forests exert a strong influence on the surface
climate of the temperate and boreal regions. Deforestation induces a significant cooling which modifies the atmospheric circulation
simulated in the high latitudes, and also in the tropics. The most important impact is observed during the melting season
which is delayed by the forest removal. This result is consistent with preliminary stand-alone experiments showing that the
atmospheric boundary layer can be heated by the forest, even if the ground is covered by snow. The study confirms that vegetation
feedbacks should be included when performing future climate studies such as doubled CO2 experiments, eventhough many uncertainties still remain with regard to other physical aspects of the climate models.
Received: 5 September 1995 / Accepted: 12 August 1996 相似文献
6.
The inter-annual variability in monthly mean summer temperatures derived from nine different regional climate model (RCM) integrations is investigated for both the control climate (1961–1990) and a future climate (2071–2100) based on A2 emissions. All regional model integrations, carried out in the PRUDENCE project, use the same boundaries of the HadAM3H global atmospheric model. Compared to the CRU TS 2.0 observational data set most RCMs (but not all) overpredict the temperature variability significantly in their control simulation. The behaviour of the different regional climate models is analysed in terms of the surface energy budget, and the contributions of the different terms in the surface energy budget to the temperature variability are estimated. This analysis shows a clear relation in the model ensemble between temperature variability and the combined effects of downward long wave, net short wave radiation and evaporation (defined as F). However, it appears that the overestimation of the temperature variability has no unique cause. The effect of short-wave radiation dominates in some RCMs, whereas in others the effect of evaporation dominates. In all models the temperature variability and F increase when imposing future climate boundary conditions, with particularly high values in central Europe. 相似文献
7.
August 2006 was an exceptionally wet month in the Netherlands, in particular near the coast where rainfall amounts exceeded
300% of the climatological mean. August 2006 was preceded by an extremely warm July with a monthly mean temperature of almost
1°C higher than recorded in any other summer month in the period 1901–2006. This had resulted in very high sea surface temperatures
(SSTs) in the North Sea at the end of July. In this paper the contribution of high SSTs to the high rainfall amounts is investigated.
In the first part of this study, this is done by analyzing short-term integrations with a regional climate model (RACMO2)
operated at 6 km resolution, which are different in the prescribed values of the SSTs. In the second part of the paper the
influence of SSTs on rainfall is analyzed statistically on the basis of daily observations in the Netherlands during the period
1958–2006. The results from both the statistical analysis as well as the model integrations show a significant influence of
SSTs on precipitation. This influence is particularly strong in the coastal area, that is, less than 30–50 km from the coastline.
With favorable atmospheric flow conditions, the analyzed dependency is about +15% increase per degree temperature rise, thereby
exceeding the Clausius–Clapeyron relation—which is often used as a temperature related constraint on changes in extreme precipitation—by
approximately a factor of two. It is shown that the coastal area has consistently become wetter compared to the inland area
since the 1950s. This finding is in agreement with the rather strong observed trend in SST over the same period and the dependencies
of rainfall on SST reported in this study. 相似文献
8.
Bayesian multi-model projection of climate: bias assumptions and interannual variability 总被引：1，自引：0，他引：1
Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations.
Application of this information to impact studies requires a combined probabilistic estimate taking into account the different
models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution
of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control
period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian
methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability.
In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent
(i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model
biases for each RCM and introduces two plausible assumptions (“constant bias” and “constant relationship”) about extrapolating
the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative
priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present
results for Alpine winter and summer temperatures for control (1961–1990) and scenario periods (2071–2100) under the SRES
A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5–3.6°C. For summer, the
two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4°C
and 3.4°C for the “constant bias” and “constant relation” assumptions, respectively. Analysis shows that the underlying reason
for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results
show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is
needed to determine how bias information can be exploited for this task. 相似文献
9.
The coupling of optimal economic growth and climate dynamics 总被引：1，自引：0，他引：1
Olivier Bahn Laurent Drouet Neil R. Edwards Alain Haurie Reto Knutti Socrates Kypreos Thomas F. Stocker Jean-Philippe Vial 《Climatic change》2006,79(1-2):103-119
In this paper, we study optimal economic growth programs coupled with climate change dynamics. The study is based on models derived from MERGE, a well established integrated assessment model (IAM). We discuss first the introduction in MERGE of a set of “tolerable window” constraints which limit both the temperature change and the rate of temperature change. These constraints, obtained from ensemble simulations performed with the Bern 2.5-D climate model, allow us to identity a domain intended to preserve the Atlantic thermohaline circulation. Next, we report on experiments where a two-way coupling is realized between the economic module of MERGE and an intermediate complexity “3-D-” climate model (C-GOLDSTEIN) which computes the changes in climate and mean temperature. The coupling is achieved through the implementation of an advanced “oracle based optimization technique” which permits the integration of information coming from the climate model during the search for the optimal economic growth path. Both cost-effectiveness and cost-benefit analysis modes are explored with this combined “meta-model” which we refer to as GOLDMERGE. Some perspectives on future implementations of these approaches in the context of “collaborative” or “community” integrated assessment modules are derived from the comparison of the different approaches. 相似文献
10.
Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal 总被引：7，自引：3，他引：4
Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization
in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile
mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors,
its ability to generate “new extremes” (values outside the calibration range), and its impact on the climate change signal
(CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias
of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order
of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation
of the error correction function enables QM to reproduce “new extremes” without deterioration and mostly with improvement
of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends
in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly
derived indices of extremes, such as threshold indices. 相似文献
11.
A comparison of estimates of the root-mean-square error (RMSE) and potential predictability index (PPI) is carried out between
experiments with observed and “persistent” anomalies of sea surface temperature (SST). The results obtained point to a possible
significant bias of seasonal forecasting results in some regions when boundary conditions are introduced by a “persistence”
procedure, particularly for summer T
850. Indirect evidence of the influence of extratropical SST anomalies points to their possible role in seasonal forecasts, which
is more substantial in the summer season. Although the conclusions should rather be regarded as preliminary ones because of
a limited size of the sample, it is nonetheless certain that the influence of boundary conditions governing the signal becomes
more significant in summer because of a decrease in the instability of the internal atmospheric dynamics. 相似文献
12.
Regional climate model sensitivity to domain size 总被引：3，自引：1，他引：2
Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions
(LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development
of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving
data, unless large scale nudging is applied. The issue of domain size is studied here by using the “perfect model” approach.
This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain
of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC.
The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same
model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared
with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric
fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction
of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability
in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 × 100 grid points). The
permanent “spatial spin-up” corresponds to the characteristic distance that the large-scale flow needs to travel before developing
small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere. 相似文献
13.
Forecast skill as a function of the ensemble size is examined in a 24-member ensemble of northern winter (DJF) hindcasts
produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis. These
integrations are initialized from the NCEP reanalyses at 6 h intervals prior to the forecast season. The sea surface temperatures
that are applied as lower boundary conditions are predicted by persisting the monthly mean anomaly observed prior to the forecast
period. The potential predictability that is attributed to lower boundary forced variability is estimated. In lagged-average
forecasting, the forecast skill in the first two weeks, which originates predominately from the initial conditions, is greatest
for relatively small ensemble sizes. The forecast skill increases monotonically with the ensemble size in the rest of the
season. The skill of DJF 500 hPa geopotential height hindcasts in the Northern Hemisphere and in the Pacific/North America
sector improves substantially when the ensemble size increases from 6 to 24. A statistical skill improvement technique based
on the singular value decomposition method is also more successful for larger ensembles.
Received: 22 February 2000 / Accepted: 6 December 2000 相似文献
14.
The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data 总被引：4，自引：1，他引：3
We study the influence of station network density on the distributions and trends in indices of area-average daily precipitation
and temperature in the E-OBS high resolution gridded dataset of daily climate over Europe, which was produced with the primary
purpose of Regional Climate Model evaluation. Area averages can only be determined with reasonable accuracy from a sufficiently
large number of stations within a grid-box. However, the station network on which E-OBS is based comprises only 2,316 stations,
spread unevenly across approximately 18,000 0.22° grid-boxes. Consequently, grid-box data in E-OBS are derived through interpolation
of stations up to 500 km distant, with the distance of stations that contribute significantly to any grid-box value increasing
in areas with lower station density. Since more dispersed stations have less shared variance, the resultant interpolated values
are likely to be over-smoothed, and extreme daily values even more so. We perform an experiment over five E-OBS grid boxes
for precipitation and temperature that have a sufficiently dense local station network to enable a reasonable estimate of
the area-average. We then create a series of randomly selected station sub-networks ranging in size from four to all stations
within the E-OBS interpolation search radii. For each sub-network realisation, we estimate the grid-box average applying the
same interpolation methodology as used for E-OBS, and then evaluate the effect of network density on the distribution of daily
values, as well as trends in extremes indices. The results show that when fewer stations have been used for the interpolation,
both precipitation and temperature are over-smoothed, leading to a strong tendency for interpolated daily values to be reduced
relative to the “true” area-average. The smoothing is greatest for higher percentiles, and therefore has a disproportionate
effect on extremes and any derived extremes indices. For many regions of the E-OBS dataset, the station density is sufficiently
low to expect this smoothing effect to be significant and this should be borne in mind by any users of the E-OBS dataset. 相似文献
15.
16.
Summary Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using
a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures,
the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials.
No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature
forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent
trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer
in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill,
while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere
distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors,
especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface
temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal
predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result
of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole
data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore,
even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational
seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed.
Received July 18, 1997 Revised April 2, 1998 相似文献
17.
The energy cycle characterizes basic aspects of the physical behaviour of the climate system. Terms in the energy cycle involve
first and second order climate statistics (means, variances, covariances) and the intercomparison of energetic quantities
offers physically motivated “second order” insight into model and system behaviour. The energy cycle components of 12 models
participating in AMIP2 are calculated, intercompared and assessed against results based on NCEP and ERA reanalyses. In general,
models simulate a modestly too vigorous energy cycle and the contributions to and reasons for this are investigated. The results
suggest that excessive generation of zonal available potential energy is an important driver of the overactive energy cycle
through “generation push” while excessive dissipation of eddy kinetic energy in models is implicated through “dissipation
pull‘’. The study shows that “ensemble model” results are best or among the best in the comparison of energy cycle quantities
with reanalysis-based values. Thus ensemble approaches are apparently “best” not only for the simulation of 1st order climate
statistics as in Lambert and Boer (Clim Dyn 17:83–106, 2001) but also for the higher order climate quantities entering the
energy cycle. 相似文献
18.
Hervé Douville 《Climate Dynamics》2005,24(4):373-391
While time-slice simulations with atmospheric general circulation models (GCMs) have been used for many years to regionalize climate projections and/or assess their uncertainties, there is still no consensus about the method used to prescribe sea surface temperature (SST) in such experiments. In the present study, the response of the Indian summer monsoon to increasing amounts of greenhouse gases and sulfate aerosols is compared between a reference climate scenario and three sets of time-slice experiments, consisting of parallel integrations for present-day and future climates. Different monthly mean SST boundary conditions have been tested in the present-day integrations: raw climatological SST derived from the reference scenario, observed climatological SST, and observed SST with interannual variability. For future climate, the SST forcing has been obtained by superimposing climatological monthly mean SST anomalies derived from the reference scenario onto the present-day SST boundary conditions. None of these sets of time-slice experiments is able to capture accurately the response of the Indian summer monsoon simulated in the transient scenario. This finding suggests that the ocean–atmosphere coupling is a fundamental feature of the climate system. Neglecting the SST feedback and variability at the intraseasonal to interannual time scales has a significant impact on the projected monsoon response to global warming. Adding interannual variability in the prescribed SST boundary conditions does not mitigate the problem, but can on the contrary reinforce the discrepancies between the forced and coupled experiments. The monsoon response is also shown to depend on the simulated control climate, and can therefore be sensitive to the use of observed rather than model-derived SSTs to drive the present-day atmospheric simulation, as well as to any approximation in the prescribed radiative forcing. While such results do not challenge the use of time-slice experiments for assessing uncertainties and understanding mechanisms in transient scenarios, they emphasize the need for high-resolution coupled atmosphere-ocean GCMs for dynamical downscaling, or at least for high-resolution atmospheric GCMs coupled with a slab or a regional ocean model. 相似文献
19.
Sensitivity of the thermohaline circulation and climate to ocean exchanges in a simple coupled model 总被引：2，自引：0，他引：2
A new simple, coupled climate model is presented and used to investigate the sensitivity of the thermohaline circulation
and climate to ocean vertical and horizontal exchange. As formulated, the model highlights the role of thin, ocean surface
layers in the communication between the atmosphere and the subsurface ocean. Model vertical exchange is considered to be an
analogue to small-scale, diapycnal mixing and convection (when present) in the ocean. Model horizontal exchange is considered
to be an analogue to the effects of the wind-driven circulation. For small vertical exchange in the ocean, the model exhibits
only one steady-state solution: a relatively cold, mid-high-latitude climate associated with a weak, salinity-driven circulation
(“off ” mode). For large vertical and horizontal exchange in the ocean, the model also exhibits only one steady-state solution:
a relatively warm, mid-high-latitude climate associated with a strong, thermally-driven circulation (“on” mode). For sufficiently
weak horizontal exchange but large enough vertical exchange, both modes are possible stable, steady-state solutions. When
model parameters are calibrated to fit tracer distributions of the modern ocean-atmosphere system, only the “on” mode is possible
in this standard case. This suggests that the wind-driven circulation in consort with diapycnal mixing suppresses the “off ”
mode in the modern ocean-atmosphere system. Since both diapycnal mixing and the wind-driven circulation would be expected
to increase in a cold climate with greater meridional temperature gradients and enhanced winds, vertical and horizontal exchange
in the ocean are probably associated with strong negative feedbacks which tend to stabilize climate. These results point to
the need to resolve ocean wind-driven circulation and to greatly improve the treatment of ocean diapycnal mixing in more complete
models of the climate system.
Received: 16 November 1999 / Accepted: 19 June 2000 相似文献
20.
The modification of greenhouse gas warming by the direct effect of sulphate aerosols 总被引：1，自引：0，他引：1
The Canadian Centre for Climate Modelling and Analysis (CCCma) second generation climate model (GCMII) consists of an atmospheric
GCM coupled to mixed layer ocean. It is used to investigate the climate response to a doubling of the CO2 concentration together with the direct effect of scattering by sulphate aerosols. As expected, the aerosols offset some of
the greenhouse gas (GHG) warming; the global annual mean screen temperature change due to doubled CO2 is 3.4 °C in this model and this is reduced to 2.7 °C when an estimate of the direct effect of anthropogenic sulphate aerosols
is included. The pattern of climate response to the comparatively localized aerosol forcing is not itself localized, and it
bears a striking resemblance to the response pattern that arises from the globally distributed change in GHG forcing. This
“non-local” response to “localized” forcing indicates that the pattern of climate response is determined, to first order,
by the overall magnitude of the change in forcing rather than its detailed nature or structure. Feedback processes operating
in the system apparently determine this pattern by locally amplifying and suppressing the response to the magnitude of the
change in forcing. The influence of the location of the change in forcing is relatively small. These “non-local” and “local”
effects of aerosol forcing are characterized and displayed and some of their consequences discussed. Effects on the moisture
budget and on the energetics of the global climate are also examined.
Received: 10 June 1997 / Accepted: 8 January 1998 相似文献