Regional climate modeling using convection-permitting models (CPMs; 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.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014RG000475

A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges.

Cities have elevated temperatures compared to rural areas, a phenomenon known as the “urban heat island”. Higher temperatures increase the risk of heat-related mortality, which will be exacerbated by climate change. To examine the impact of climate change and urban growth on future urban temperatures and the potential for increased heat stress on urban residents. We conducted a systematic review of scientific articles from Jan 2000 to May 2016. The majority (n = 49, = 86%) of studies examined climate change and the urban heat island in isolation, with few (8) considering their combined effect. Urban growth was found to have a large impact on local temperatures, in some cases by up to 5 °C in North-east USA. In some locations climate change increased the heat island, such as Chicago and Beijing, and in others decreased it, such as Paris and Brussels. When the relative impact of both factors was considered, the temperature increase associated with the urban heat island was always higher. Few studies (9) considered heat stress and its consequences for urban populations. Important contributors to urban temperatures, such as variation in urban density and anthropogenic heat release, were often excluded from studies. We identify a need for an increased research focus on (1) urban growth impact on the urban heat island in climate change studies; (2) heat stress; and, (3) variation in urban density and its impacts on anthropogenic heat. Focussing on only one factor, climate change or urban growth, risks underestimating future urban temperatures and hampering adaptation.

The impact of urbanization and climate change on urban temperatures: a systematic review

Regional climate modeling is a dynamical downscaling technique applied to the results of global climate models (GCMs) in order to acquire more information on climate simulations and climate change projections. GCMs and regional climate models (RCMs) have undergone considerable development over the past few decades, and both have increased in resolution. The higher-resolution edge of RCMs compared to GCMs still remains, however. This has been demonstrated in a number of specific studies. As GCMs operate on relatively coarse resolutions, they do not resolve more variable land forms and similar features that shape regional-scale climates. RCMs operate on higher resolutions than GCMs, by a factor of 2-10. Some RCMs now explore resolutions down to 1-5 km. This adds value in regions with variable orography, land-sea and other contrasts, as well as in capturing sharp, short-duration and extreme events. In contrast, large-scale and time-averaged fields, not least over smooth terrain and on scales that have been already skillfully resolved in GCMs, are not much affected. RCMs also generate additional detail compared to GCMs when in climate projection mode. Compared to the present-day climate for which observations exist, here the added value aspect is more complex to evaluate. Nevertheless, added value is meaningfully underlined when there is a clear physical context for it to appear in. In addition to climate modeling and model evaluation-related added value considerations, a significant relevant aspect of added value is the provision of regional scale information, including climate change projections, for climate impact, adaptation, and vulnerability research. (C) 2015 Wiley Periodicals, Inc. (Less)

Added value in regional climate modeling

. Many shallow landslides and debris flows are precipitation initiated.
Therefore, regional landslide hazard assessment is often based on empirically
derived precipitation intensity-duration (ID) thresholds and landslide
inventories. Generally, two features of precipitation events are plotted and
labeled with (shallow) landslide occurrence or non-occurrence. Hereafter, a
separation line or zone is drawn, mostly in logarithmic space. The practical
background of ID is that often only meteorological information is available
when analyzing (non-)occurrence of shallow landslides and, at the same time,
it could be that precipitation information is a good proxy for both
meteorological trigger and hydrological cause. Although applied in many case
studies, this approach suffers from many false positives as well as limited
physical process understanding. Some first steps towards a more
hydrologically based approach have been proposed in the past, but these
efforts received limited follow-up. Therefore, the objective of our paper is to (a) critically analyze the
concept of precipitation ID thresholds for shallow landslides and debris
flows from a hydro-meteorological point of view and (b) propose a
trigger–cause conceptual framework for lumped regional hydro-meteorological
hazard assessment based on published examples and associated discussion. We
discuss the ID thresholds in relation to return periods of precipitation,
soil physics, and slope and catchment water balance. With this paper, we aim
to contribute to the development of a stronger conceptual model for regional
landslide hazard assessment based on physical process understanding and
empirical data.

/pdf/invited-perspectives-hydrological-perspectives-on-4r9reb89a1.pdf

Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds

We propose an approach to spatial modeling of extreme rainfall, based on max-stable processes fitted using partial duration series and a censored threshold likelihood function. The resulting models are coherent with classical extreme-value theory and allow the consistent treatment of spatial dependence of rainfall using ideas related to those of classical geostatistics. We illustrate the ideas through data from the Val Ferret watershed in the Swiss Alps, based on daily cumulative rainfall totals recorded at 24 stations for four summers, augmented by a longer series from nearby. We compare the fits of different statistical models appropriate for spatial extremes, select that best fitting our data, and compare return level estimates for the total daily rainfall over the stations. The method can be used in other situations to produce simulations needed for hydrological models, and in particular, for the generation of spatially heterogeneous extreme rainfall fields over catchments.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/wrcr.20329

Threshold modeling of extreme spatial rainfall

A new high-resolution dynamical downscaling strategy to examine how rural and urban areas respond to change in future climate, is presented. The regional climate simulations have been performed with a new version of the limited-area model of the ARPEGE-IFS system running at 4 km resolution coupled with the Town Energy Balance (TEB) scheme. To downscale further the regional climate projections to a urban scale, at 1-km resolution, a stand-alone surface scheme is employed in offline mode. We performed downscaling simulations according to three model set-ups: (1) reference run, where TEB is not activated neither in 4 km simulations nor in 1 km urban simulation, (2) offline run, where TEB is activated only for 1 km urban simulation and (3) inline run, where TEB is activated both for regional and urban simulations. The applicability of this method is demonstrated for Brussels Capital Region, Belgium. For present climate conditions, another set of simulations were performed using European Center for Medium-Range Weather Forecasts global reanalysis ERA40 data. Results from our simulations indicate that the reference and offline runs have comparable values of daytime and nocturnal urban heat island (UHI) and lower values than the inline run. The inline values are closer to observations. In the future climate, under and A1B emission scenario, the three downscaling methods project a decrease of daytime UHI between −0.24 and −0.20 °C, however, their responses are different for nocturnal UHI: (1) reference run values remains unaltered, (2) for the offline runs, the frequency of present climate weak nocturnal UHI decreases to the benefit of negative UHIs leading to a significant decrease in the nocturnal UHI over the city and (3) for the inline run, nocturnal UHIs stays always positive but the frequency of the strong UHI decreases significantly in the future by 1 °C. The physical explanation is put forth. Copyright © 2013 Royal Meteorological Society

Assessment of three dynamical urban climate downscaling methods: Brussels's future urban heat island under an A1B emission scenario

The Gaussian copula model for the joint deficit index for droughts

Bayesian estimation of rainfall intensity-duration-frequency relationships

[1] Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the T year return level. Extreme precipitation depths in Belgium are analyzed for accumulation durations ranging from 10 min to 30 days. Spatial generalized extreme value (GEV) models are presented by considering multisite data and relating GEV parameters to geographical/climatological covariates through a common regression relationship. Methods of combining data from several sites are in common use, and in such cases, there is likely to be nonnegligible intersite dependence. However, parameter estimation in GEV models is generally done with the maximum likelihood estimation method (MLE) that assumes independence. Estimates of uncertainty are adjusted for spatial dependence using methodologies proposed earlier. Consistency of GEV distributions for various durations is obtained by fitting a smooth function to the preliminary estimations of the shape parameter. Model quality has been assessed by various statistical tests and indicates the relevance of our approach. In addition, a methodology is applied to account for the fact that measurements have been made in fixed intervals (usually 09:00 UTC–09:00 UTC). The distribution of the annual sliding 24 h maxima was specified through extremal indices of a more than 110 year time series of 24 h aggregated 10 min rainfall and daily rainfall. Finally, the selected models are used for producing maps of precipitation return levels.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2011WR011707

Spatial regression models for extreme precipitation in Belgium

We explore the use of high-resolution dynamical downscaling as a means to simulate extreme values of summer maximum surface air temperature over Belgium (TMAX). We use the limited area version of the ARPEGE-IFS model, ALADIN. Our approach involves a sequence of daily integrations driven by perfect boundary conditions at the lateral boundaries provided by the ERA40 reanalysis. In this study, three different recent past (1961–1990) simulations are evaluated against different station datasets: (1) 40 km spatial resolution (ALD40), (2) 10 km spatial resolution (ALD10), and (3) 4 km spatial resolution (ALR04) using a new parameterisation of deep convection, and microphysics allowing the use of ALADIN at resolutions ranging from a few tens of kilometers down to less than 4 km. The validation of ALD40 reveals a positive summer bias (2.2 °C), even though the considerable spatial resolution enhancement by a factor of 4, ALD10 reduces slightly the warm biases (1.7 °C). This warm bias on TMAX is strongly correlated with cloud cover representation. Result shows an overestimate of clear-sky occurrences by ALD10 and a developing solar radiation overestimate through the diurnal cycle, with 116 W m−2 maximum overestimate at noon. ALR04 reduces considerably the warm biases (−0.2 °C) which suggests of its ability to simulate weakly forced convective cloud in the summer over Belgium. In addition, the Generalized Pareto Distribution (GPD) of the extreme high temperatures produced by the different simulations has been compared to observations of the same period. ALD40 and ALD10 gave a GPD distribution that did not replicate the observed distribution well and, thus, overestimated the extremes. This study shows that the consistent treatment of deep convection and cloud–radiation interaction when increasing the horizontal resolution is very important when studying extremely high temperatures events. Copyright © 2011 Royal Meteorological Society

H. Van de Vyver

Papers

Assessment of three dynamical urban climate downscaling methods: Brussels's future urban heat island under an A1B emission scenario

The Gaussian copula model for the joint deficit index for droughts

Bayesian estimation of rainfall intensity-duration-frequency relationships

Spatial regression models for extreme precipitation in Belgium

New cloud and microphysics parameterisation for use in high-resolution dynamical downscaling: application for summer extreme temperature over Belgium