Jahreszeitenvorhersagen mit dem DWD - MPI-M - UHH Vorhesagesystem

Weather and Climate Dynamics, 2021

Abstract. We investigate the impact of the strength of the Atlantic Meridional Overturning Circulation (AMOC) at 26∘ N on the prediction of North Atlantic sea surface temperature anomalies (SSTAs) a season ahead. We test the dependence of sea surface temperate (SST) predictive skill in initialised hindcasts on the phase of the AMOC at 26∘N, invoking a seesaw mechanism driven by AMOC fluctuations, with positive SSTAs north of 26∘ N and negative SSTAs south of 26∘ N after a strong AMOC and vice versa. We use initialised simulations with the MPI-ESM-MR (where MR is mixed resolution) seasonal prediction system. First, we use an assimilation experiment between 1979–2014 to confirm that the AMOC leads a SSTA dipole pattern in the tropical and subtropical North Atlantic, with the strongest AMOC fingerprints after 2–4 months. Going beyond previous studies, we find that the AMOC fingerprint has a seasonal dependence and is sensitive to the length of the observational window used, i.e. strong...

International Journal of Climatology, 2016

Regional climate models (RCMs) include both terrestrial and atmospheric compartments and thereby allow studying land-atmosphere feedback, in particular, the impact of land-use land cover driven by biogeophysical processes on regional climate. In this study, a method is developed to separate the signals from the noise in RCM simulations of the effects of changes in land use, using perturbed initial boundary conditions (PICs). We want to know how many ensemble members are required to identify robust and statistically significant land-use land cover change (LULCC) effects from RCM LULCC studies. The method is applied to a case study of urbanization and deforestation, for which LULCC scenarios are implemented in the RCM Weather Research and Forecasting (WRF). Based on WRF ensemble simulations with PICs for 2010, the signal-to-noise ratio (SNR) is used to identify areas with pronounced effect of an LULCC or, rather, the parametrization of the land-use classes. While in the urbanization scenarios clear and statistically significant signals are found for air temperature and for both latentand sensible heat (SNR values up to 24), the effects are less pronounced for precipitation, and for deforestation in general (SNR values < 1). For the case study of urbanization and precipitation, the impact of the ensemble size is studied in order to derive robust conclusions about the effects of LULCC on precipitation. We conclude that single RCM realizations of different land-use representations are not sufficient to derive LULCC-induced signals, particularly not for precipitation. Small ensemble sizes led to concluding there were significant LULCC-induced precipitation signals, but these disappeared when the ensemble size was increased. Our regional analysis suggests the need for ensemble sizes well above 10 for precipitation.

Geophysical Research Letters, 2016

We investigate how observational uncertainty in satellite‐retrieved sea ice concentrations affects seasonal climate predictions. To do so, we initialize hindcast simulations with the Max Planck Institute Earth System Model every 1 May and 1 November from 1981 to 2011 with two different sea ice concentration data sets, one based on the NASA Team and one on the Bootstrap algorithm. For hindcasts started in November, initial differences in Arctic sea ice area and surface temperature decrease rapidly throughout the freezing period. For hindcasts started in May, initial differences in sea ice area increase over time. By the end of the melting period, this causes significant differences in 2 meter air temperature of regionally more than 3°C. Hindcast skill for surface temperatures over Europe and North America is higher with Bootstrap initialization during summer and with NASA Team initialization during winter. This implies that the observational uncertainty also affects forecasts of tele...

Geophysical Research Letters, 2018

Climate and weather variability in the North Atlantic region is determined largely by the North Atlantic Oscillation (NAO). The potential for skillful seasonal forecasts of the winter NAO using an ensemble-based dynamical prediction system has only recently been demonstrated. Here we show that the winter predictability can be significantly improved by refining a dynamical ensemble through subsampling. We enhance prediction skill of surface temperature, precipitation, and sea level pressure over essential parts of the Northern Hemisphere by retaining only the ensemble members whose NAO state is close to a "first guess" NAO prediction based on a statistical analysis of the initial autumn state of the ocean, sea ice, land, and stratosphere. The correlation coefficient between the reforecasted and observation-based winter NAO is significantly increased from 0.49 to 0.83 over a reforecast period from 1982 to 2016, and from 0.42 to 0.86 for a forecast period from 2001 to 2017. Our novel approach represents a successful and robust alternative to further increasing the ensemble size, and potentially can be used in operational seasonal prediction systems. Plain Language Summary Predicting Northern Hemisphere winter conditions, which are controlled largely by fluctuations in the pressure filed over the North Atlantic (North Atlantic Oscillation, NAO), for the next season is a major challenge. Most state-of-the-art seasonal prediction systems show a correlation between observed and predicted NAOs of less than 0.30. Our novel approach uses dynamical links (teleconnections) between the autumn state of sea surface temperature in the North Atlantic, Arctic sea ice, snow in Eurasia, and stratosphere temperature over the Northern Hemisphere as predictors of the NAO in the subsequent winter to subsample a dynamical reforecast ensemble. We select only the ensemble members that consistently reproduce winter NAO states that evolve in accordance with the autumn state of these predictors. As a result the winter NAO prediction skill increases to a correlation value of 0.83. Considering these well established NAO teleconnections in our Earth system model leads to an improved prediction skill of European winter conditions, that is, surface temperature, precipitation, and sea level pressure. Our results advance seasonal prediction of European weather to a level that is usually limited to tropical regions and are relevant for a variety of societal sectors, such as global and national economies and energy and water resources.

Geophysical Research Letters, 2018

We evaluate the impact of a new five-layer soil-hydrology scheme on seasonal hindcast skill of 2 m temperatures over Europe obtained with the Max Planck Institute Earth System Model (MPI-ESM). Assimilation experiments from 1981 to 2010 and 10-member seasonal hindcasts initialized on 1 May each year are performed with MPI-ESM in two soil configurations, one using a bucket scheme and one a new five-layer soil-hydrology scheme. We find the seasonal hindcast skill for European summer temperatures to improve with the five-layer scheme compared to the bucket scheme and investigate possible causes for these improvements. First, improved indirect soil moisture assimilation allows for enhanced soil moisture-temperature feedbacks in the hindcasts. Additionally, this leads to improved prediction of anomalies in the 500 hPa geopotential height surface, reflecting more realistic atmospheric circulation patterns over Europe.