Decomposition of the large-scale atmospheric state driving downscaling: a perspective on dynamical downscaling for regional climate study

Nishizawa, Seiya; Adachi, Sachiho A.; Kajikawa, Yoshiyuki; Yamaura, Tsuyoshi; Ando, Kazuto; Yoshida, Ryuji; Yashiro, Hisashi; Tomita, Hirofumi

doi:10.1186/s40645-017-0159-0

Cited by 5 publications

(16 citation statements)

References 52 publications

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“…The number of TCs approaching Japan is projected to reduce, whereas the TC precipitation rate increases under the future climate. This is consistent with the projection by AGCM20 (Lui et al 2018) and the projections using dynamical downscaling (Knutson et al 2015;Huang et al 2016;Nishizawa et al 2018). The frequency of moderate TC precipitation, which sometimes occurs under the present climate, does not change significantly because of the cancelation of these two contrary effects.…”

Section: Discussionsupporting

confidence: 89%

“…The changes in the total TC precipitation and the frequency distribution depend on the balance between two competing factors: reduction in TC number and intensification of TC precipitation rate. In Nishizawa et al (2018), in which dynamical downscaling data for the projection by AGCM20 with C0 SST distribution was used, the reduction of TC number surpasses the intensification of precipitation, and a reduction of total TC precipitation are projected, which is consistent with our result for C0 SST. Using the projection by AGCM20 that is used for the boundary condition of NHRCM05, Kitoh and Endo (2016) showed that the annual maximum daily precipitation associated with TCs will decrease in the future owing to a reduction in TC number.…”

Section: Discussionsupporting

confidence: 87%

“…Recently, the evaluation of changes in TC precipitation using dynamical downscaling by an operational hurricane model (Knutson et al 2015) or a RCM has been started (Huang et al 2016;Nishizawa et al 2018). Among them, Nishizawa et al (2018) evaluated the future changes in TC precipitation over Japan. However, they performed a dynamical downscaling from only one member of ensemble simulations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Future Projection of Tropical Cyclone Precipitation over Japan with a High-Resolution Regional Climate Model

Watanabe

Murata²,

Sasaki³

et al. 2019

Journal of the Meteorological Society of Japan

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This study evaluates possible changes in tropical cyclone (TC) precipitation over Japan under a future warmer climate using an ensemble projection generated by a non-hydrostatic regional climate model with a resolution of 5 km (NHRCM05) under the RCP8.5 scenario. NHRCM05 reproduces TC precipitation and TC intensity more accurately than does a general circulation model with a resolution of 20 km. The number of TCs approaching Japan is projected to decrease under the future climate, while the TC precipitation rate increases. As these two effects cancel each other out, total TC precipitation, and the frequency of the moderate TC precipitation that is usual under the present climate, shows no significant change. On the other hand, the frequency of extreme TC precipitation increases significantly because the intensification of the TC precipitation rate outweighs the reduction in TC frequency. The increase in the TC precipitation rate is caused primarily by the increase in water vapor around the TCs, which in turn results from the increase in environmental water vapor. The intensification and structural changes to TCs also contribute to the enhanced TC precipitation.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Future Projection of Tropical Cyclone Precipitation over Japan with a High-Resolution Regional Climate Model

Watanabe

Murata²,

Sasaki³

et al. 2019

Journal of the Meteorological Society of Japan

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“…These large‐scale climate states are shown as diamonds on the climatology–perturbation phase space in Figure a, according to A2017 and Nishizawa et al . (), and they are used as RCM boundary conditions in our analysis. The vertical axis indicates the changes in a regional climate target variable that are estimated from downscaling experiments.…”

Section: Methodsmentioning

confidence: 99%

An evaluation method for uncertainties in regional climate projections

Adachi

Nishizawa

Ando

et al. 2018

Atmospheric Science Letters

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Regional climate projections inevitably inherit uncertainties from general circulation models (GCMs). We therefore propose a new approach for identifying the dominant uncertainties. This approach employs the downscaling procedure by Adachi et al. to the uncertainty problem using multiple GCM projections. The mean state of the large-scale atmospheric states and the deviation from this mean state are the two uncertainty factors considered here, which are provided by a GCM. These two factors are referred to as climatology and perturbation components, respectively. To demonstrate the effectiveness in identifying these uncertainty factors using the proposed approach, a regional projection of summertime climate in western Japan is conducted using four different future climate data that are calculated using an atmospheric GCM with different sea surface temperatures. Results show that the variability in surface air temperature projections is reasonably derived from the climatology uncertainty, whereas the variability in precipitation projections is equally influenced by the climatology and perturbation uncertainties. Both the climatology and perturbation uncertainties should therefore be considered when analysing regional climate projections. K E Y W O R D Sdynamical downscaling, regional climate model, regional climate projection uncertainties, uncertainty evaluation | INTRODUCTIONIt is important to elucidate uncertainties that persist in regional climate projections when designing adaptation plans to anticipate future climate change. The sources of uncertainty in future climate projections are primarily divided into three categories Sutton, 2009, 2011): internal variability, the greenhouse gas emission scenario and imperfections in general circulation models (GCMs). Uncertainty related to internal variability is present in the projections regardless of global warming and its influence decreases relative to climate change signal due to global warming when longer prediction lead times are considered. Differences in the greenhouse gas emissions affect the radiative balance of the atmosphere. Uncertainties due to GCM imperfections occur because GCMs generally exhibit different climate sensitivities to certain external forces (e.g., greenhouse gases) and exhibit different spatial patterns in the atmospheric fields even if the global climate sensitivities are of the same degree. Uncertainties due to imperfections in regional climate models (RCMs) should also be considered in regional climate projections that employ downscaling simulations (Wilby and Dessai, 2010) because they can show different downscaled climates even if the RCMs use the same

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“…Nishizawa et al () proposed the interpretation of the downscaling procedure using a conceptual phase‐space diagram for the climatology and perturbation components. According to the proposal, a constraint to an RCM can be established by combining two components from different data sets.…”

Section: Conceptual Idea and Mathematical Descriptionmentioning

confidence: 99%

Methodology of the Constraint Condition in Dynamical Downscaling for Regional Climate Evaluation: A Review

Adachi

Tomita

2020

JGR Atmospheres

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The dynamical downscaling method with a regional climate model (RCM) is widely used to assess the spatially detailed information about regional climate. However, the RCM result is considerably influenced by the systematic errors inherent to a general circulation model (GCM), which provides the initial and boundary conditions to the RCM. Such systematic errors sometimes lead to meaningless downscaled results. Many modified boundary dynamical downscaling (MBDDS) methods have been proposed to reduce the influences of the systematic errors of a GCM and extract meaningful signals for regional climate change. This study comprehensively reviews the MBDDS methods. The MBDDS methods partially modify the climate information projected by a GCM and use them as the boundary conditions of an RCM. The objectives of the methods are organized into two main objectives, that is, to obtain more reliable projections by correcting the biases in boundary conditions and to better understand the regional climate change mechanisms. To ensure comprehensive understanding of the MBDDS methods, this study attempts to interpret the errors included in the downscaled results using mathematical expressions, separating the GCM‐originated bias and RCM's own bias. Using this analysis, the MBDDS methods are classified based on the following questions: What effect is expected from the bias correction? Which of the climate change components projected by a GCM is considered when assessing the future climate change? The direction and issues that need to be addressed in the future for better understanding the regional climate change are also discussed.

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Decomposition of the large-scale atmospheric state driving downscaling: a perspective on dynamical downscaling for regional climate study

Cited by 5 publications

References 52 publications

Future Projection of Tropical Cyclone Precipitation over Japan with a High-Resolution Regional Climate Model

Future Projection of Tropical Cyclone Precipitation over Japan with a High-Resolution Regional Climate Model

An evaluation method for uncertainties in regional climate projections

Methodology of the Constraint Condition in Dynamical Downscaling for Regional Climate Evaluation: A Review

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