Global atmospheric reanalyses have become a common tool for both validation of climate models and diagnostic studies, such as assessing climate variability and long-term trends. Presently, the ...Twentieth Century Reanalysis (20CR), which assimilates only surface pressure reports, sea ice, and sea surface temperature distributions, represents the longest global reanalysis dataset available covering the period from 1871 to the present. Currently the 20CR dataset is extensively used for the assessment of climate variability and trends. Here, the authors compare the variability and long-term trends in northeast Atlantic storminess derived from 20CR and from observations. A well-established storm index derived from pressure observations over a relatively densely monitored marine area is used. It is found that both variability and long-term trends derived from 20CR and from observations are inconsistent. In particular, both time series show opposing trends during the first half of the twentieth century: both storm indices share a similar behavior only for the more recent periods. While the variability and long-term trend derived from the observations are supported by a number of independent data and analyses, the behavior shown by 20CR is quite different, indicating substantial inhomogeneities in the reanalysis, most likely caused by the increasing number of observations assimilated into 20CR over time. The latter makes 20CR likely unsuitable for the identification of trends in storminess in the earlier part of the record, at least over the northeast Atlantic. The results imply and reconfirm previous findings that care is needed in general when global reanalyses are used to assess long-term changes.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Regional climate modeling using convection‐permitting models (CPMs; horizontal grid spacing <4 km) emerges as a promising framework to provide more reliable climate information on regional to local ...scales compared to traditionally used large‐scale models (LSMs; horizontal grid spacing >10 km). CPMs no longer rely on convection parameterization schemes, which had been identified as a major source of errors and uncertainties in LSMs. Moreover, CPMs allow for a more accurate representation of surface and orography fields. The drawback of CPMs is the high demand on computational resources. For this reason, first CPM climate simulations only appeared a decade ago. In this study, we aim to provide a common basis for CPM climate simulations by giving a holistic review of the topic. The most important components in CPMs such as physical parameterizations and dynamical formulations are discussed critically. An overview of weaknesses and an outlook on required future developments is provided. Most importantly, this review presents the consolidated outcome of studies that addressed the added value of CPM climate simulations compared to LSMs. Improvements are evident mostly for climate statistics related to deep convection, mountainous regions, or extreme events. The climate change signals of CPM simulations suggest an increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains. In conclusion, CPMs are a very promising tool for future climate research. However, coordinated modeling programs are crucially needed to advance parameterizations of unresolved physics and to assess the full potential of CPMs.
Key Points
Convection‐permitting climate models reduce errors in large‐scale models
Added value in convective processes, regional extremes, and over mountains
Discusses challenges/potentials of convection‐permitting climate simulations
Spectral nudging is a method that was developed to constrain regional climate models so that they reproduce the development of the large-scale atmospheric state, while permitting the formation of ...regional-scale details as conditioned by the large scales. Besides keeping the large-scale development in the interior close to a given state, the method also suppresses the emergence of ensemble variability. The method is mostly applied to reconstructions of past weather developments in regions with an extension of typically 1000–8000 km. In this article, the authors examine if spectral nudging is having an effect on simulations with model regions of the size of about 700 km × 500 km at midlatitudes located mainly over flat terrain. First two pairs of simulations are compared, two runs each with and without spectral nudging, and it is found that the four simulations are very similar, without systematic or intermittent phases of divergence. Smooth fields, which are mainly determined by spatial patterns, such as air pressure, show hardly any differences, while small-scale and heterogeneous fields such as precipitation vary strongly, mostly on the gridpoint scale, irrespective if spectral nudging is employed or not. It is concluded that the application of spectral nudging has little effect on the simulation when the model region is relatively small.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like ...polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties).
However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales.
Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Tropical cyclone (TC) activity for the last three decades shows strong discrepancies, deduced from different best track datasets (BTD) for the western North Pacific (WNP). This study ...analyzes the reliability of BTDs in deriving climate statistics for the WNP. Therefore, TC lifetime, operational parameters current intensity (CI) number, and tracks are compared (for TCs identified concurrently) in BTD provided by the Joint Typhoon Warning Center (JTWC), the Japan Meteorological Agency (JMA), and the China Meteorological Administration (CMA).
The differences between the BTD are caused by varying algorithms used in weather services to estimate TC intensity. Available methods for minimizing these discrepancies are not sufficient. Only if intensity categories 2–5 are considered as a whole, do trends for annually accumulated TC days show a similar behavior. The reasons for remaining discrepancies point to extensive and not regular usage of supplementary sources in JTWC. These are added to improve the accuracy of TC intensity and center position estimates. Track and CI differences among BTDs coincide with a strong increase in the number of intense TC days in JTWC. These differences are very strong in the period of intensive improvement of spatiotemporal satellite coverage (1987–99).
Scatterometer-based data used as a reference show that for the tropical storm phase JMA provides more reliable TC intensities than JTWC. Comparisons with aircraft observations indicate that not only homogeneity, but also a harmonization and refinement of operational rules controlling intensity estimations, should be implemented in all agencies providing BTD.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The "spectral nudging" method imposes time-variable large-scale atmospheric states on a regional atmospheric model. It is based on the idea that regional-scale climate statistics are conditioned by ...the interplay between continental-scale atmospheric conditions and such regional features as marginal seas and mountain ranges. Following this "downscaling" idea, the regional model is forced to satisfy not only boundary conditions, possibly in a boundary sponge region, but also large-scale flow conditions inside the integration area. In the present paper the performance of spectral nudging in an extended climate simulation is examined. Its success in keeping the simulated state close to the driving state at larger scales, while generating smaller-scale features is demonstrated, and it is also shown that the standard boundary forcing technique in current use allows the regional model to develop internal states conflicting with the large-scale state. It is concluded that spectral nudging may be seen as a suboptimal and indirect data assimilation technique.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Abstract
This study explores the possibility of reconstructing the weather of Southeast Asia for the last decades using an atmospheric regional climate model, the Climate version of the Lokal-Modell ...(CLM). For this purpose global National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses data were dynamically downscaled to 50 km and in a double-nesting approach to 18-km grid distance. To prevent the regional model from deviating significantly from the reanalyses with respect to large-scale circulation and large-scale weather phenomena, a spectral nudging technique was used.
The performance of this technique in dealing with Southeast Asian typhoons is now examined by considering an ensemble of one simulated typhoon case. This analysis is new insofar as it deals with simulations done in the climate mode (so that any skill of reproducing the typhoon is not related to details of initial conditions), is done in ensemble mode (the same development is described by several simulations), and is done with a spectral nudging constraint (so that the observed large-scale state is enforced in the model domain). This case indicates that tropical storms that are coarsely described by the reanalyses are correctly identified and tracked; considerably deeper core pressure and higher wind speeds are simulated compared to the driving reanalyses. When the regional atmospheric model is run without spectral nudging, significant intraensemble variability occurs; also additional, nonobserved typhoons form. Thus, the insufficiency of lateral boundary conditions alone for determining the details of the dynamic developments in the interior becomes very clear. The same lateral boundary conditions are consistent with different developments in the interior. Several sensitivity experiments were performed concerning varied grid distances, different initial starting dates of the simulations, and changed spectral nudging parameters.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Long-term atmospheric changes are a result of complex interactions on various spatial scales. In this study, we examine the long-term variability of the most important meteorological variables in a ...convection-permitting regional climate model simulation. A consistent, gridded data set from 1948 to 2014 was computed using the regional climate model COSMO-CLM with a very high convection-permitting resolution at a grid distance of 2.8 km, for a region encompassing the German Bight and Northern Germany. This is one of the very first atmospheric model simulations with such high resolution, and covering several decades. Using a very high-resolution hindcast, this study aims to extend knowledge of the significance of regional details for long-term variability and multi-decadal trends of several meteorological variables such as wind, temperature, cloud cover, precipitation, and convective available potential energy (CAPE). This study demonstrates that most variables show merely large decadal variability and no long-term trends. The analysis shows that the most distinct and significant positive trends occur in temperature and in CAPE for annual mean values as well as for extreme events. No clear and no significant trend is detectable for the annual sum of precipitation and for extreme precipitation. However, spatial structures in the trends remain weak.
Simulations of a Regional Climate Model (RCM) driven by identical lateral boundary conditions but initialized at different times exhibit the phenomenon of so-called internal model variability (or in ...short, Internal Variability—IV), which is defined as the inter-member spread between members in an ensemble of simulations. Our study investigates the effects of air-sea coupling on IV of the regional atmospheric model COSMO-CLM (CCLM) of the new regional coupled system model GCOAST-AHOI (Geesthacht Coupled cOAstal model SysTem: Atmosphere, Hydrology, Ocean and Sea Ice). We specifically address physical processes parameterized in CCLM, which may cause a large IV during an extreme event, and where this IV is affected by the air-sea coupling. Two six-member ensemble simulations were conducted with GCOAST-AHOI and the stand-alone CCLM (CCLM_ctr) for a period of 1 September–31 December 2013 over Europe. IV is expressed by spreads within the two sets of ensembles. Analyses focus on specific events during this period, especially on the storm Christian occurring from 27 to 29 October 2013 in northern Europe. Results show that simulations of CCLM_ctr vary largely amongst ensemble members during the storm. By analyzing two members of CCLM_ctr with opposite behaviors, we found that the large uncertainty in CCLM_ctr is caused by a combination of two factors (1) uncertainty in parameterization of cloud-radiation interaction in the atmospheric model. and (2) lack of an active two-way air-sea interaction. When CCLM is two-way coupled with the ocean model, the ensemble means of GCOAST-AHOI and CCLM_ctr are relatively similar, but the spread is reduced remarkably in GCOAST-AHOI, not only over the ocean where the coupling is done but also over land due to the land-sea interactions.
Continental to global‐scale modeling of the carbon cycle using process‐based models is subject to large uncertainties. These uncertainties originate from the model structure and uncertainty in model ...forcing fields; however, little is known about their relative importance. A thorough understanding and quantification of uncertainties is necessary to correctly interpret carbon cycle simulations and guide further model developments. This study elucidates the effects of different state‐of‐the‐art land cover and meteorological data set options and biosphere models on simulations of gross primary productivity (GPP) over Europe. The analysis is based on (1) three different process‐oriented terrestrial biosphere models (Biome‐BGC, LPJ, and Orchidee) driven with the same input data and one model (Biome‐BGC) driven with (2) two different meteorological data sets (ECMWF and REMO), (3) three different land cover data sets (GLC2000, MODIS, and SYNMAP), and (4) three different spatial resolutions of the land cover (0.25° fractional, 0.25° dominant, and 0.5° dominant). We systematically investigate effects on the magnitude, spatial pattern, and interannual variation of GPP. While changing the land cover map or the spatial resolution has only little effect on the model outcomes, changing the meteorological drivers and especially the model results in substantial differences. Uncertainties of the meteorological forcings affect particularly strongly interannual variations of simulated GPP. By decomposing modeled GPP into their biophysical and ecophysiological components (absorbed photosynthetic active radiation (APAR) and radiation use efficiency (RUE), respectively) we show that differences of interannual GPP variations among models result primarily from differences of simulating RUE. Major discrepancies appear to be related to the feedback through the carbon‐nitrogen interactions in one model (Biome‐BGC) and water stress effects, besides the modeling of croplands. We suggest clarifying the role of nitrogen dynamics in future studies and revisiting currently applied concepts of carbon‐water cycle interactions regarding the representation of canopy conductance and soil processes.
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