Kristina Fröhlich

Seasonal predictability over Europe arising from El Niño and stratospheric variability in the MPI-ESM seasonal prediction system

AbstractPredictability on seasonal time scales over the North Atlantic–Europe region is assessed using a seasonal prediction system based on an initialized version of the Max Planck Institute Earth System Model (MPI-ESM). For this region, two of the dominant predictors on seasonal time scales are El Nino–Southern Oscillation (ENSO) and sudden stratospheric warming (SSW) events. Multiple studies have shown a potential for improved North Atlantic predictability for either predictor. Their respective influences are however difficult to disentangle, since the stratosphere is itself impacted by ENSO. Both El Nino and SSW events correspond to a negative signature of the North Atlantic Oscillation (NAO), which has a major influence on European weather.This study explores the impact on Europe by separating the stratospheric pathway of the El Nino teleconnection. In the seasonal prediction system, the evolution of El Nino events is well captured for lead times of up to 6 months, and stratospheric variability is re...

The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

AbstractA seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2–4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.

Observations and Ray Tracing of Gravity Waves: Implications for Global Modeling

Vertical coupling by atmospheric waves is essential for the wind and temperature structure of the middle atmosphere. In particular, momentum carried by atmospheric gravity waves (GWs) governs the global circulation in the mesosphere and is for instance the reason for the cold summer mesopause. However, the small horizontal scales of GWs (tens to thousands of km) are challenging both global modeling and observations from satellite. Further, due to the small scales involved, there is a severe lack of understanding about GWs themselves, as well as dynamical phenomena involving GWs. Until recently, global observations of GWs were sparse and little was known about the global distribution of GWs, as well as their seasonal variation. Therefore, several projects in the priority program Climate And Weather of the Sun-Earth System (CAWSES) of the Deutsche Forschungsgemeinschaft (DFG) have addressed a number of the most pressing problems. Global distributions of GW activity and momentum fluxes have been derived from observations with number of satellite instruments, resulting in the first multi-year global data sets of GW parameters, covering time scales from seasonal variations up to the duration of almost a full 11-year solar cycle. In addition, seasonal and tidal variations of sporadic E layers in the ionosphere were studied in Global Positioning System (GPS) radio occultation data. Satellite observations of GWs and sporadic E layers were complemented by ground-based observations (radar and low-frequency (LF) drift measurements). All these observations, as well as accompanying modeling activities provided important constraints for GW parameterizations. Further activities addressed important aspects of GW propagation usually neglected in global modeling: GW ray tracing studies revealed the importance of non-vertical propagation of GWs and first steps were undertaken to develop an improved GW parameterization based on GW ray tracing techniques.

Seasonal climate forecasts significantly affected by observational uncertainty of Arctic sea ice concentration

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 teleconnections that depend on northern hemispheric climate indices.

Searching for an Added Value of Precipitation in Downscaled Seasonal Hindcasts over East Africa: COSMO-CLM Forced by MPI-ESM

Downscaling of seasonal hindcasts over East Africa with the regional climate model (RCM) COSMO-CLM (CCLM), forced by the global climate model (GCM) and MPI-ESM, is evaluated. The simulations are done for five months (May to September) for a ten-year period (2000–2009), with the evaluation performed only for June to September. The dry years, 2002 and 2009, and the wet years, 2006 and 2007, are well captured by the models. By using ground based and satellite gridded observation data for evaluation it is found that both COSMO-CLM and MPI-ESM overestimate June to September precipitation over the Ethiopian highlands and in parts of the lowland with respect to all reference datasets. In addition we investigated the potential and real added value for both the RCM and the GCM hindcasts by upscaling (arithmetic mean) the precipitation resolution both in temporal and in spatial scales, over North Ethiopia (EN), South Ethiopia (ES), South Sudan (SS), and Sudan (S). Results inferred that using the RCM for seasonal forecast adds value in capturing extreme precipitation years, especially in the Ethiopian highlands. It is also found that the potential and relative potential added value decrease with decreasing the temporal resolution.

Improved Seasonal Prediction of European Summer Temperatures With New Five‐Layer Soil‐Hydrology Scheme

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.

Improved teleconnection-based dynamical seasonal predictions of boreal winter

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.