Ingo Bethke

Mechanism on how the spring Arctic sea ice impacts the East Asian summer monsoon

Observational analysis and purposely designed coupled atmosphere–ocean (AOGCM) and atmosphere-only (AGCM) model simulations are used together to investigate a new mechanism describing how spring Arctic sea ice impacts the East Asian summer monsoon (EASM). Consistent with previous studies, analysis of observational data from 1979 to 2009 show that spring Arctic sea ice is significantly linked to the EASM on inter-annual timescales. Results of a multivariate Empirical Orthogonal Function analysis reveal that sea surface temperature (SST) changes in the North Pacific play a mediating role for the inter-seasonal connection between spring Arctic sea ice and the EASM. Large-scale atmospheric circulation and precipitation changes are consistent with the SST changes. The mechanism found in the observational data is confirmed by the numerical experiments and can be described as follows: spring Arctic sea ice anomalies cause atmospheric circulation anomalies, which, in turn, cause SST anomalies in the North Pacific. The SST anomalies can persist into summer and then impact the summer monsoon circulation and precipitation over East Asia. The mediating role of SST changes is highlighted by the result that only the AOGCM, but not the AGCM, reproduces the observed sea ice-EASM linkage.

Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment

Here, we firstly demonstrate the potential of an advanced flow dependent data assimilation method for performing seasonal-to-decadal prediction and secondly, reassess the use of sea surface temperature (SST) for initialisation of these forecasts. We use the Norwegian Climate Prediction Model (NorCPM), which is based on the Norwegian Earth System Model (NorESM) and uses the deterministic ensemble Kalman filter to assimilate observations. NorESM is a fully coupled system based on the Community Earth System Model version 1, which includes an ocean, an atmosphere, a sea ice and a land model. A numerically efficient coarse resolution version of NorESM is used. We employ a twin experiment methodology to provide an upper estimate of predictability in our model framework (i.e. without considering model bias) of NorCPM that assimilates synthetic monthly SST data (EnKF-SST). The accuracy of EnKF-SST is compared to an unconstrained ensemble run (FREE) and ensemble predictions made with near perfect (i.e. microscopic SST perturbation) initial conditions (PERFECT). We perform 10 cycles, each consisting of a 10-yr assimilation phase, followed by a 10-yr prediction. The results indicate that EnKF-SST improves sea level, ice concentration, 2 m atmospheric temperature, precipitation and 3-D hydrography compared to FREE. Improvements for the hydrography are largest near the surface and are retained for longer periods at depth. Benefits in salinity are retained for longer periods compared to temperature. Near-surface improvements are largest in the tropics, while improvements at intermediate depths are found in regions of large-scale currents, regions of deep convection, and at the Mediterranean Sea outflow. However, the benefits are often small compared to PERFECT, in particular, at depth suggesting that more observations should be assimilated in addition to SST. The EnKF-SST system is also tested for standard ocean circulation indices and demonstrates decadal predictability for Atlantic overturning and sub-polar gyre circulations, and heat content in the Nordic Seas. The system beats persistence forecast and shows skill for heat content in the Nordic Seas that is close to PERFECT.

Investigating the recent apparent hiatus in surface temperature increases: 2. Comparison of model ensembles to observational estimates

To assess published hypotheses surrounding the recent slowdown in surface warming (hiatus), we compare five available global observational surface temperature estimates to two 30-member ensembles from the Norwegian Earth System Model (NorESM). Model ensembles are initialized in 1980 from the transient historical runs and driven with forcings used in the CMIP5 experiments and updated forcings based upon current observational understanding, described in Part 1. The ensembles’ surface temperature trends are statistically indistinguishable over 1998–2012 despite differences in the prescribed forcings. There is thus no evidence that forcing errors play a significant role in explaining the hiatus according to NorESM. The observations fall either toward the lower portion of the ensembles or, for some observational estimates and regions, outside. The exception is the Arctic where the observations fall toward the upper ensemble bounds. Observational data set choices can make a large difference to findings of consistency or otherwise. Those NorESM ensemble members that exhibit Nino3.4 Sea Surface Temperature (SST) trends similar to observed also exhibit comparable tropical and to some extent globalmean trends, supporting a role for El Nino Southern Oscillation in explaining the hiatus. Several ensemble members capture the marked seasonality observed in Northern Hemispheremidlatitude trends, with cooling in the wintertime and warming in the remaining seasons. Overall, we find that we cannot falsify NorESM as being capable of explaining the observed hiatus behavior. Importantly, this is not equivalent to concluding NorESM could simultaneously capture all important facets of the hiatus. Similar experiments with further, distinct, Earth System Models are required to verify our findings.

Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model

We document a pilot stochastic re-analysis computed by assimilating sea surface temperature (SST) anomalies into the ocean component of the coupled Norwegian Climate Prediction Model (NorCPM) for the period 1950–2010 (doi: 10.11582/2016.00002). NorCPM is based on the Norwegian Earth System Model and uses the ensemble Kalman filter for data assimilation (DA). Here, we assimilate SST from the stochastic HadISST2 historical reconstruction. The accuracy, reliability and drift are investigated using both assimilated and independent observations. NorCPM is slightly overdispersive against assimilated observations but shows stable performance through the analysis period. It demonstrates skills against independent measurements: sea surface height, heat and salt content, in particular in the Equatorial and North Pacific, the North Atlantic Subpolar Gyre (SPG) region and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the vertical temperature variability there, in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using a flow-dependent assimilation method and constructing the covariance in isopycnal coordinates are investigated in the SPG region. Isopycnal coordinates discretisation is found to better capture the vertical structure than standard depth-coordinate discretisation, because it leads to a deeper influence of the assimilated surface observations. The vertical covariance shows a pronounced seasonal and decadal variability that highlights the benefit of flow-dependent DA method. This study demonstrates the potential of NorCPM to compute an ocean re-analysis for the 19th and 20th centuries when SST observations are available.

Investigating the recent apparent hiatus in surface temperature increases: 1. Construction of two 30-member Earth System Model ensembles

The recent Intergovernmental Panel on Climate Change report, along with numerous studies since, has suggested that the apparent global warming hiatus results from some combination of natural variability and changes to external forcings. Herein the external forcings for greenhouse gases (GHGs), long-lived trace gases, volcanic and tropospheric aerosols, and solar irradiance have been replaced in the Norwegian Earth System Model using recent observational estimates. The potential impact of these alternative forcings, and by residual the internally generated variability, is examined through two 30-member ensembles covering the period 1980 to 2012. The Reference ensemble uses the Coupled Model Intercomparison Project phase 5 historical forcings extended with the Representative Concentration Pathway 8.5 (RCP8.5) scenario, while the Sensitivity ensemble uses the alternative forcings. Over the hiatus period defined herein as 1998–2012, all of the forcings show some change between the Sensitivity and Reference experiments and have a combined net forcing change of −0.03 W m−2. The GHG forcing is 0.012 W m−2 higher in the Sensitivity forcings. The alternative solar forcing differs from the Reference forcing by −0.08 W m−2, the same as the alternative volcanic forcing that was based on the latest estimates from NASA Goddard Institute for Space Studies. Anthropogenic aerosol emissions were replaced using the EU-EclipseV4a data set and produce a mean forcing change of 0.11 W m−2 over the period. Part 1 details the creation of the two 30-member ensembles and their characterization for parameters of particular relevance to the explanation of the hiatus. A detailed investigation of the two resulting ensembles global surface temperature behavior is given in Part 2, along with comparisons to observational data sets.

A production-tagged aerosol module for Earth system models, OsloAero5.3 – extensions and updates for CAM5.3-Oslo

The article untitled “A production-tagged aerosol module for earth system models, OsloAero5.3 – extensions and updates for CAM5.3-Oslo” by A. Kirkevag et al. presents in a very detailed way updates in the modelisation of aerosols that is used in the atmospheric component of the Norwegian Earth System Model (NorESM). This updated version called OsloAero5.3 is here tested in the CAMS5.3 atmospheric model which is part of the Community Earth System Model 1.2 (CESM). With regards to the CMIP6 project, OsloAero5.3 is planned to be integrated/merged with CEMS2 to form the NorESM2 model, but the version presented in this article could be used for the early phase of CMIP6. Therefore, in addition to being of value to the aerosol modelling community, the discussions in the article are fully relevant to the CMIP6 exercise.

Description and evaluation of NorESM1-F: A fast version of theNorwegian Earth System Model (NorESM)

A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g. 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multi-millennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speedup is 5 primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean-sea ice horizontal grid configuration that allows an increase of the ocean-sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasises model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F 10 in general agree well with observational estimates, and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain. 15

Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model

Abstract A data assimilation method capable of constraining the sea ice of an Earth system model in a dynamically consistent manner has the potential to enhance the accuracy of climate reconstructions and predictions. Finding such a method is challenging because the sea ice dynamics is highly non-linear, and sea ice variables are strongly non-Gaussian distributed and tightly coupled to the rest of the Earth system – particularly thermodynamically with the ocean. We investigate key practical implementations for assimilating sea ice concentration – the predominant source of observations in polar regions – with the Norwegian Climate Prediction Model that combines the Norwegian Earth System Model with the Ensemble Kalman Filter. The performances of the different configurations are investigated by conducting 10-year reanalyses in a perfect model framework. First, we find that with a flow-dependent assimilation method, strongly coupled ocean–sea ice assimilation outperforms weakly coupled (sea ice only) assimilation. An attempt to prescribe the covariance between the ocean temperature and the sea ice concentration performed poorly. Extending the ocean updates below the mixed layer is slightly beneficial for the Arctic hydrography. Second, we find that solving the analysis for the multicategory instead of the aggregated ice state variables greatly reduces the errors in the ice state. Updating the ice volumes induces a weak drift in the bias for the thick ice category that relates to the postprocessing of unphysical thicknesses. Preserving the ice thicknesses for each category during the assimilation mitigates the drift without degrading the performance. The robustness and reliability of the optimal setting is demonstrated for a 20-year reanalysis. The error of sea ice concentration reduces by 50% (65%), sea ice thickness by 25% (35%), sea surface temperature by 33% (23%) and sea surface salinity by 11% (25%) in the Arctic (Antarctic) compared to a reference run without assimilation.

Pacific contribution to the early twentieth-century warming in the Arctic

Arctic surface temperature warmed more than twice as fast as global temperature during the early twentieth century, similar to that during the recent global warming. This Arctic warming has been attributed to both external forcing1 and internal variability associated with atmospheric dynamics2,3 and Atlantic Ocean temperature4 in combination with Pacific variability5. Here we show, through coupled climate model experiments that superpose externally forced and dynamically driven changes, that Pacific decadal variability alone was a key contributor to the early twentieth century Arctic warming. Sea surface temperatures in the model are phased to observations by prescribing historical wind variations over the Pacific, which drive thermodynamically consistent decadal variations. During the early twentieth century, the Pacific Decadal Oscillation (PDO) transitioned to a positive phase with a concomitant deepening of the Aleutian Low that warms the Arctic by poleward low-level advection of extratropical air. In addition, our experiments revealed that the implemented Pacific surface changes weaken the polar vortex, which leads to subsidence-induced adiabatic heating of the Arctic surface. Thus, our results suggest that the observed recent shift to the positive PDO phase6 will intensify Arctic warming in the forthcoming decades.In the early twentieth century, the Arctic warmed faster than the global average. Pacific Ocean interdecadal variability, specifically wind-driven sea surface temperatures, drove the Arctic warming through enhanced heat transport.

Global Freshwater Availability Below Normal Conditions and Population Impact Under 1.5 and 2 °C Stabilization Scenarios

Based on the large ensembles of the half a degree additional warming, prognosis, and projected impacts historical, +1.5 and +2 °C experiments, we quantify changes in the magnitude of water availability (i.e., precipitation minus actual evapotranspiration; a function of monthly precipitation flux, latent heat flux, and surface air temperature) below normal conditions (less than median, e.g., 20th percentile water availability). We found that, relative to the historical experiment, water availability below normal conditions of the +1.5 and +2 °C experiments would decrease in the midlatitudes and the tropics, indicating that hydrological drought is likely to increase in warmer worlds. These cause more (less) people in East Asia, Central Europe, South Asia, and Southeast Asia (West Africa and Alaska/Northwest Canada) to be exposed to water shortage. Stabilizing warming at 1.5 °C instead of 2 °C would limit population impact in most of the regions, less effective in Alaska/Northwest Canada, Southeast Asia, and Amazon. Globally, this reduced population impact is ~117 million people. Plain Language Summary This study emerges from the lack of scientific investigations to inform climate policy about differences between two global warming targets (i.e., 1.5 and 2 °C) for the “Intergovernmental Panel on Climate Change Special Report on Global Warming of 1.5°C.” We seek to understand the following: How would water availability below normal conditions (the drier end of hydrological extremes) change at these targets? Howwould they affect the water shortage of human society? Could we limit the impact by stabilizing the global warming at 1.5 °C instead of 2 °C? To address these questions, we employ the HAPPI (half a degree additional warming, prognosis, and projected impacts) experiments, explicitly designed to differentiate impacts between these targets. Relative to the historical period, future water availability below normal conditions (less than median, e.g., 20th percentile or lower) would decrease in the midlatitudes and the tropics; the globe and most of the regions would endure water shortages. Relative to the 2 °C warming target, stabilizing temperature increase at 1.5 °C would constrain adverse impact on people suffering water shortages in most of the regions (particularly Central Europe, East Africa, East Asia, South Asia, and West Africa) but ineffective in Alaska/Northwest Canada, Southeast Asia, and Amazon. A global sum of this reduced risk is ~117 million people.