Recent Regional Climate State and Change - Derived through Downscaling Homogeneous Large-scale Components of Re-analyses Hans von Storch, Beate Geyer, Katharina Klehmet, Li Delei, Martina Schubert-Frisius, Nelle Tim and Eduardo Zorita Institute of Coastal Research 26 May College of Marine Sciences, Shanghai Ocean University (SHOU), Shanghai

VON STORCH Hans 1.Climate researcher (in the field since 1971) 2.Coastal climate (storms, storm surges, waves; North and Baltic Sea, North Atlantic, Yellow Sea); statistical analysis 3.Retired director of the Institute of Coastal Research of the Helmholtz Zentrum Geesthacht, Germany 4.Professor at Universität Hamburg 5.Guest professor at the 中国海洋大学, 青岛 china/Hans von Storch.china.htm

Climate = statistics of weather The genesis of climate C s = f(C l, Φ s ) with C l = larger scale climate C s = smaller scale climate Φ s = physiographic detail at smaller scale  “downscaling”

Data driven modeling...

Pattern correlation coefficients for V at 500 hPa between the global reanalyses and the RCM with standard forcing via the lateral boundaries and the RCM with spectral nudging global regional Nudging of the large scales

Improved presentation of in coastal and marine regions NCEP1 and ERA-I-driven multidecadal simulations with regional RCM CCLM over East Asia ( 李德磊, 2015), the Southern Atlantic (Tim and Zorita), the Lena Delta (Klehmet) NCEP1 driven simulation with global GCM ECHAM6 (Schubert-Frisius, Feser) Grid resolutions: about 50 km and 7 km Employing spectral nudging (wind above 850, 750 hPa, for scales > 800 km and 1300 km) Using station data for reference Usage of Quikscat-windfields (QS) over sea as a reference Determining Brier Skill score for all marine grid boxes B = 1 – (RCM-QS) 2 / (ERA-QS) 2

Brier Skill score for marine surface wind speeds of (a) 3 – 25 m/s and (b) 10.8 – 25 m/s, comparing the downscaled product (with a regional model) and the driving ERA-Interim reanalysis. QuikSCAT wind data is used as “true” field. Positive values indicate a more realistic description of the downscaled product than of the driving reanalysis. 李德磊

Comparison with in-situ data: offshore station 10 The statistics of CCLM winds (left) show no improvement relative to ERA-I winds (right) in offshore station IEO, red line stands for Q-Q plot. 李德磊

11 Comparison with in-situ data: coastal station The statistics of CCLM winds (left) show much better results than that of ERA-I winds (right) in coastal station SEO; red line stands for Q-Q plot. 李德磊

Brier Skill score for snow water equivalent in January and April in Central Siberia, comparing the downscaled product (with a regional model) and the driving NCEP1-re analysis. As reference (“truth”) ESA GlobSnow is used. Positive values indicate a more realistic description of the downscaled product than of the driving re-analysis. Klehmet

Modified Brier Skill Score for daily mean near-surface wind speed in the period December1999 to November of the regional downscaling compared to the ERA- interim re-analysis data with QuikSCAT near-surface wind as reference. Red areas indicate a better performance of the downscaled data than the re-analysis data. Only daily mean speeds between 3 m/sec and 25 m/s were included in this calculation Tim & Zorita

Improved representation of sub-synoptic phenomena Polar Lows in the Northern North Pacific ( 陈飞 et al., 2012, 2013, 2014) Polar lows in the Northern North Atlantic (Zahn et al.) Medicanes in the Mediterranean Sea (Cavicchia et al.) Typhoons in the West Pacific (Feser, Barcikowska et al.) Synoptic and sub-synoptic variability in the BoHai and Huang Hai ( 李德磊 et al., 2016a,b)

North Pacific Polar Low on 7 March 1977 NOAA-5 infrared satellite image at 09:58UTC 7th March 1977 North Pacific Polar Lows ( 陈飞 et al., 2012, 2013 and 2014) Allows assessment of changes in recent decades and expected in the coming decades

Spatial distributions of wind speed (m/s) and vectors around Jeju Island at 12:00 UTC and 18:00 UTC 17 January 1997 for (a, b) ERA-Interim reanalysis data and (c, d) regional model downscaling product. 李德磊

Global downscaling by constraining large scales Following the ideas of Yoshimura and Kanamitsu (2008) ECHAM6 (T255L95) simulation with NCEP1 reanalysis (T62L28) constraining large scales (> 1300 km). Constraining with a nudging corresponding to an e-folding time of 50 min’s above 750 hPa The nudging coefficients are set to G n ~ 1/50 min for n n nudge for heights above 750 hPa (plateau-formed profile) Variables constrained: vorticity and divergence Output Grid size is 0.46 degree (~50km), 1 hourly output for 67 years Available at DKRZ since 2015

Global distribution of BSS, comparing the global downscaling data with the driving NCEP1 reanalysis, for 10m wind speed for the year ERA-Interim re-analysis data are used as reference. Contour interval: 0.2. Schubert-Frisius et al

Time series of nudged ECHAM6 and observed station data (DWD) for 10m wind speed at Hamburg Fuhlsbüttel (Germany) in the year Schubert Frisius et al.

Brier Skill Scores over the period of Dec – Nov for marine surface wind speeds of (left panel) 3 – 25 m/s and (right panel) 10.8 – 25 m/s relative to “truth” reference QuikSCAT data. Comparison … of the global downscaling product and the driving NCEP1 … of the regional (55 km) and the global downscaling product … of the regional (7 km) and the global downscaling product

Monthly (January and April) Brier Skill Scores over the period of of snow water equivalent relative to ESA GlobSnow as observational reference data (“truth”) for Central Siberia in January and April. Klehmet Comparison of the regional and the global downscaling product Red areas indicate a superior performance of the regional product compared to the global product.

Brier skill scores comparing the regional (with CCLM_16 km) and global downscaling product for daily mean wind speeds between 8-25 m/s. As reference, QuikSCAT analysis is used. Time window: 01/12/ /11/2009 Tim & Zorita

Improved representation of forcing fields for impact models NCEP-driven multidecadal simulation with RCM REMO in Europe Grid resolution: 0.5 o Employing spectral nudging (wind above 850 hPa, for scales > 800 km) simulation Wind and air pressure used to drive models of sea level and circulation of marginal seas (not shown) for describing currents and sea level Wind used to drive models of the statistics of surface waves (ocean waves) in coastal seas (North Sea).

The CoastDat-effort at the Institute for Coastal  Long-term, high-resolution reconstructions (60 years) of present and recent developments of weather related phenomena in coastal regions as well as scenarios of future developments (100 years)  Northeast Atlantic and northern Europe.  Assessment of changes in storms, ocean waves, storm surges, currents and regional transport of anthropogenic substances.  Extension to other regions and to ecological parameters. Applications  many authorities with responsibilities for different aspects of the German coasts  economic applications by engineering companies (off-shore wind potentials and risks) and shipbuilding company  Public information Integration area used in HZG reconstruction and regional scenarios

simuliert Extreme wind events simulated compared to local observations Weisse, pers. comm.

Annual mean winter high waters Cuxhaven red – reconstruction, black – observations Interannual variability of mean water levels (Weisse and Plüß 2006)

Red: buoy, yellow: radar, blue: wave model run with REMO winds wave direction significant wave height [days] Gerd Gayer, pers. comm., 2001

Range of changes in seasonal maximum wind (10 m) in Northern Germany in a series of dynamically downscaled scenarios (HadCM, MPI; A2, B2) Regional scenarios of maximum sustained winds for Northern Germany

Wave Energy Flux [kW/m] Currents Power [W/m 2 ] Some applications of - Ship design - Navigational safety - Offshore wind - Interpretation of measurements - Oils spill risk and chronic oil pollution - Ocean energy - Scenarios of storm surge conditions - Scenarios of future wave conditions Weisse, pers. comm.

Conclusion: downscaling adds cognitive values Dynamical downscaling works … - Large scales are hardly affected but smaller scales respond to regional physiographic detail. Medium scales are determined by both the large scale dynamics and the regional physiographic details (C s = f(C l,Φ s )) Added value in medium scales (in particular coastal regions). Added value in describing medium scale phenomena – in particular storms Added value in generating regional impact variables, in particular wind for storm surges and ocean waves. Downscaling allows the generation of homogeneous data sets (i.e., data sets of uniform quality across many decades of years)

Conclusion: gobal vs. regional Dynamically downcaling via the addition of large-scale constraining terms is nowadays common practice. The same has been suggested for global models by Yoshimura and Kanamitsu (2008). The effort may be seen as adding realistic additional small scale detail to the global large-scale re-analyses. The global downscaling returns have comparable skill to similar regional output. With increased computer power, global re-analyses can deal better with regional detail. With increased computer power, global downscaling is a viable option. Regional downscaling will remain an valuable tool, when going to smaller and smaller scales.