Capability of the SMHI‐RCA4 RCM driven by the ERA‐Interim reanalysis to simulate heat waves in Argentina

Collazo, Soledad; Lhotka, Ondřej; Rusticucci, Matilde; Kyselý, Jan

doi:10.1002/joc.5190

Cited by 9 publications

(3 citation statements)

References 48 publications

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“…In practice, SHMI uses RCA4 to dynamically downscale all the GCMs over the Africa CORDEX domain [79]. The RCA4 model has been applied in previous studies in Africa [25] and other parts of the world [80]. Ref.…”

Section: Data 221 Climate Data From Model Simulations Outputsmentioning

confidence: 99%

Analysis of Climate Change Projections for Mozambique under the Representative Concentration Pathways

et al. 2021

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Despite having contributed the least to global warming and having the lowest emissions, the African region is the most vulnerable continent to climate change impacts. To reduce the levels of risk arising from climate change, it is mandatory to combine both mitigation and adaptation. While mitigation can reduce global warming, not all impacts can be avoided. Therefore, adaptation is essential to advance strategic interventions and reduce the impacts. As part of the international effort to cope with changing climate, a set of Coordinated Regional Downscaling Experiment (CORDEX) domains have been established worldwide. The CORDEX-Africa initiative has been developed to analyze downscaled regional climate data over the African domain for climate data analysis techniques and engage users of climate information in both sector-specific and region/space-based applications. This study takes outputs of high-resolution climate multi-models from the CORDEX-Africa initiative constructed at a spatial resolution of 50 km to assess climate change projections over Mozambique. Projected spatial and temporal changes (three 30-year time periods, the present (2011-2040), mid (2041-2070), and the end (2071-2100)) in temperature and precipitation under the Representative Concentration Pathways RCP2.6, RCP4.5, and RCP8.5 are analyzed and compared relative to the baseline period (1961-1990). Results show that there is a tendency toward an increase in annual temperature as we move toward the middle and end of the century, mainly for RCP4.5 and RCP8.5 scenarios. This is evident for the Gaza Province, north of the Tete Province, and parts of Niassa Province, where variations will be Tmax (0.92 to 4.73 °C), Tmin (1.12 to 4.85 °C), and Tmean (0.99 to 4.7 °C). In contrast, the coastal region will experience less variation (values < 0.5 °C to 3 °C). At the seasonal scale, the pattern of temperature change does not differ from that of the annual scale. The JJA and SON seasons present the largest variations in temperature compared with DJF and MAM seasons. The increase in temperature may reach 4.47 °C in DJF, 4.59 °C in MAM, 5.04 °C in JJA, and 5.25 °C in SON. Precipitation shows substantial spatial and temporal variations, both in annual and seasonal scales. The northern coastal zone region shows a reduction in precipitation, while the entire southern region, with the exception of the coastal part, shows an increase up to 40% and up to 50% in some parts of the central and northern regions, in future climates for all periods under the three reference scenarios. At the seasonal scale (DJF and MAM), the precipitation in much of Mozambique shows above average precipitation with an increase up to more than 40% under the three scenarios. In contrast, during the JJA season, the three scenarios show a decrease in precipitation. Notably, the interior part will have the largest decrease, reaching a variation of -60% over most of the Gaza, Tete, and Niassa Provinces.

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Section: Data 221 Climate Data From Model Simulations Outputsmentioning

confidence: 99%

Analysis of Climate Change Projections for Mozambique under the Representative Concentration Pathways

et al. 2021

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“…It is well known that simulating or projecting these extremes to the local scale is very challenging (Huth et al, 2000;Maraun et al, 2010;Qian et al, 2010;Kotlarski et al, 2014;Cardoso et al, 2018). This remains true for the most up-todate RCM simulations (Chan et al, 2018;Collazo et al, 2018;Wang et al, 2018). The proposed downscaling approach, however, has capacity in translating RCM outputs of precipitation and temperature from model resolution to the local scale, preserving the local variability, particularly for extremes.…”

Section: Discussionmentioning

confidence: 99%

Downscaling climate change of mean climatology and extremes of precipitation and temperature: Application to a Mediterranean climate basin

Zhang

Corte‐Real

Moreira

et al. 2019

Intl Journal of Climatology

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Downscaling is usually necessary for robust hydrological impact assessments. This may be undertaken using a wide range of methods, including a combination of dynamical and statistical‐stochastic downscaling. This study uses the Spatial–Temporal Neyman‐Scott Rectangular Pulses model—RainSimV3, the precipitation‐conditioned daily weather generator—ICAAM‐WG, and the change factor approach for downscaling synthetic climate scenarios for robust hydrological impact assessment at middle‐sized basins. The ICAAM‐WG was developed based on the concept of the Climate Research Unit daily weather generator (CRU‐WG), motivated by the need for improved representation of heat waves by downscaling methods given the positive feedback between low soil moisture and high air temperature. We demonstrated the validity of the proposed methodology in the 705‐km2 Mediterranean climate basin in southern Portugal. The results show that, for the control period 1980–2010, both RainSimV3 and ICAAM‐WG reproduced not only the mean climatology, but also extreme wet and low precipitation events, as well as the extremes of temperature and heat waves. We found that downscaling with ICAAM‐WG (SIM6), which uses second‐order autoregressive processes for the simulation of temperature during consecutive dry and wet days, outperformed ICAAM‐WG (SIM4), which used only first‐order autoregressive processes, leading to improved simulation of heat waves. ICAAM‐WG (SIM6) well reproduced observed heatwave extremes with return periods of up to 30 years; however, ICAAM‐WG (SIM4) overestimated these extremes substantially. This indicates the importance of incorporating second‐order autoregressive processes in the simulation of heatwave length. In the context of climate warming, the proposed methodology provides a tool to improve downscaled projections of future extremes with confidence intervals for not only wet events but also dry spells and heat waves.

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“…This includes the modification of convective scheme based on Bechtold-Kain-Fritsch scheme (Bechtold et al, 2001) with revised calculation of convective available potential energy (CAPE) profile according to Jiao and Jones (2008) , and the introduction of turbulent kinetic energy (TKE) scheme (Lenderink and Holtslag, 2004) . The RCA4 model has been applied in many regions worldwide, among them Europe Kotlarski et al, 2015) , the Arctic (Berg et al, 2013;Koenigk et al, 2015;Zhang et al, 2014) , Africa (Nikulin et al, 2018;Wu et al, 2016) , South America (Collazo et al, 2018;Wu et al, 2017) , South-East (Tangang et al, 2018) and South Asia (Iqbal et al, 2017) .…”

Section: Rca4mentioning

confidence: 99%

The impact of RCM formulation and resolution on simulated precipitation in Africa

Nikulin

Kjellström

et al. 2019

Preprint

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Abstract. We investigate the impact of model formulation and horizontal resolution on the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa. Two RCMs – SMHI-RCA4 and HCLIM38-ALADIN are utilized for downscaling the ERA-Interim reanalysis over Africa at four different resolutions: 25, 50, 100 and 200 km. Additionally to the two RCMs, two different configurations of the same RCA4 are used. Contrasting different RCMs, configurations and resolutions it is found that model formulation has the primary control over many aspects of the precipitation climatology in Africa. Patterns of spatial biases in seasonal mean precipitation are mostly defined by model formulation while the magnitude of the biases is controlled by resolution. In a similar way, the phase of the diurnal cycle is completely controlled by model formulation (convection scheme) while its amplitude is a function of resolution. Although higher resolution in many cases leads to smaller biases in the time mean climate, the impact of higher resolution is mixed. An improvement in one region/season (e.g. reduction of dry biases) often corresponds to a deterioration in another region/season (e.g. amplification of wet biases). The experiments confirm a pronounced and well known impact of higher resolution – a more realistic distribution of daily precipitation. Even if the time-mean climate is not always greatly sensitive to resolution, what the time-mean climate is made up of, higher order statistics, is sensitive. Therefore, the realism of the simulated precipitation increases as resolution increases. Our results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation related improvements are strongly model dependent and in general cannot be considered as an added value of downscaling.

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Capability of the SMHI‐RCA4 RCM driven by the ERA‐Interim reanalysis to simulate heat waves in Argentina

Cited by 9 publications

References 48 publications

Analysis of Climate Change Projections for Mozambique under the Representative Concentration Pathways

Analysis of Climate Change Projections for Mozambique under the Representative Concentration Pathways

Downscaling climate change of mean climatology and extremes of precipitation and temperature: Application to a Mediterranean climate basin

The impact of RCM formulation and resolution on simulated precipitation in Africa

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