Linking the global and the regional ‐ what means global warming regionally in the Baltic Sea catchment? Hans von Storch Institute for Coastal Research, GKSS, Geesthacht and KlimaCampus, Hamburg, Germany Göteborg, 15 October 2010

Deconstructing a given record with the intention to identify „predictable“ components. „Predictable“ -- either natural processes, which are known of having limited life times, -- or man-made processes, which are subject to decisions (e.g., GHG, urban effect) Differently understood in different social and scientific quarters. The issue is also to help to discriminate between culturally supported claims and scientifically warranted.

What is this? Omstedt, 2005

The question if we „see something“ supporting the reality of a human influence on climate – needs the adoption of a mathematical language. Determination of man-made climate change is not a matter of theory, but of assessing data. The framework is of statistical nature, and the results are probability statements condition upon certain assumptions. The whole process is called „detection and attribution“.

„Significant“ trends Often, an anthropogenic influence is assumed to be in operation when trends are found to be „significant“. If the null-hypothesis is correctly rejected, then the conclusion to be drawn is – if the data collection exercise would be repeated, then we may expect to see again a similar trend. Example: N European warming trend “April to July” as part of the seasonal cycle. It does not imply that the trend will continue into the future (beyond the time scale of serial correlation). Example: Usually September is cooler than July.

„Significant“ trends Establishing the statistical significance of a trend may be a necessary condition for claiming that the trend would represent evidence of anthropogenic influence. Claims of a continuing trend require that the dynamical cause for the present trend is identified, and that the driver causing the trend itself is continuing to operate. Thus, claims for extension of present trends into the future require - empirical evidence for an ongoing trend, and - theoretical reasoning for driver-response dynamics, and - forecasts of future driver behavior.

Detection of the presence of non-natural signals: rejection of null hypothesis that recent trends are drawn from the distribution of trends given by the historical record. Statistical proof. Attribution of cause(s): Non-rejection of the null hypothesis that the observed change is made up of a sum of given signals. Plausibility argument. History: Hasselmann, K., 1979: On the signal-to-noise problem in atmospheric response studies. Meteorology over the tropical oceans (B.D.Shaw ed.), pp , Royal Met. Soc., Bracknell, Berkshire, England. Hasselmann, K., 1993: Optimal fingerprints for the detection of time dependent climate change. J. Climate 6, Hasselmann, K., 1998: Conventional and Bayesian approach to climate change detection and attribution. Quart. J. R. Meteor. Soc. 124: Detection and attribution of non-natural ongoing change

Where does the stochasticity come from? Simulation data: internally generated by a very large number of chaotic processes. Dynamical “cause” for real world’s natural unforced variability best explained as in models. Stochasticity is a mathematical construct to allow an efficient description of the (simulated and observed) climate variability.

In the 1990s … weak, not well documented signals. Example: Near-globally distributed air temperature IDAG (2005), Hegerl et al. (1996), Zwiers (1999) In the 2000s … strong, well documented signals Examples: Rybski et al. (2006) Zorita et al. (2009) IDAG, 2005: Detecting and attributing external influences on the climate system. A review of recent advances. J. Climate 18, Hegerl, G.C., H. von Storch, K. Hasselmann, B.D. Santer, U. Cubasch, P.D. Jones, 1996: Detecting anthropogenic climate change with an optimal fingerprint method. J. Climate 9, Zwiers, F.W., 1999: The detection of climate change. In: H. von Storch and G. Flöser (Eds.): Anthropogenic Climate Change. Springer Verlag, , ISBN Rybski, D., A. Bunde, S. Havlin,and H. von Storch, 2006: Long-term persistence in climate and the detection problem. Geophys. Res. Lett. 33, L06718, doi: /2005GL Zorita, E., T. Stocker and H. von Storch: How unusual is the recent series of warm years? Geophys. Res. Lett. Cases of Global Climate Change Detection Studies

Trend in air temperature Signal or noise? Hegerl et al., 1996

“Guess patterns” The reduction of degrees of freedom is done by projecting the full signal S on one or a few several “guess patterns” G k, which are assumed to describe the effect of a given driver. S =  k  k G k + n with n = undescribed part. Example: guess pattern supposedly representative of increased CO 2 levels Hegerl et al., 1996

How do we determine the „natural climate variability“? With the help of the limited empirical evidence from instrumental observations, possibly after suitable extraction of the suspected „non-natural“ signal. By accessing long „control runs“ done with quasi-realistic climate models.

Trends in temp until 1995 Trends in a scenario calculation until 2100 Hegerl et al., 1996

The ellipsoids enclose non-rejection regions for testing the null hypothesis that the 2- dimensional vector of signal amplitudes estimated from observations has the same distribution as the corresponding signal amplitudes estimated from the simulated trends in the greenhouse gas, greenhouse gas plus aerosol and solar forcing experiments. Zwiers, F.W., 1999: The detection of climate change. In: H. von Storch and G. Flöser (Eds.): Anthropogenic Climate Change. Springer Verlag, , ISBN Attribution diagram for observed 50- year trends in JJA mean temperature.

attribution From: Hadley Center, IPCC TAR, 2001

Global mean air temperature Statistics of ΔT L,m, which is the difference of two m-year temperature means separated by L years. Temperature variations are modelled as Gaussian long- memory process, fitted to various reconstructions of historical temperature (Moberg, Mann, McIntyre) Historical Reconstructions – their significance for “detection” The Rybski et al- approach

Temporal development of  T i (m,L) = T i (m) – T i-L (m) divided by the standard deviation of the m-year mean reconstructed temp record for m=5 and L=20 (top), and for m=30 and L=100 years. The thresholds R = 2, 2.5 and 3σ are given as dashed lines. Historical Reconstructions – their significance for “detection” Rybski et al., 2006

Counting extremely warm years Zorita, et al 2009 Among the last 17 years, , there were the 13 warmest years since 1880 (i.e., in 127 samples) – how probable is such an event if the time series were stationary? Monte-Carlo simulations taking into account serial correlation, either AR(1) (with lag-1 correlation  ) or long-term memory process (with Hurst parameter H=0.5+d). Best guesses   0.8 d  0.3 (very uncertain)

Zorita, et al., 2009 Log-probability of the event E that the m largest values of 157 values occupy the last17 places in long-term autocorrelation synthetic series Derived from Hadley Center/CRU data for „Giorgi bins“.

Regional: Intention: Preparation and design of measures to adapt to expected adverse effects of climate change. Problems: high variability, little knowledge about natural variability; more human- related drivers (e.g. industrial aerosols, urban effects)

21 Observations – Interpolated land station data – Temperature: CRUTEM 3v – Precipitation: GPCC v4 Simulations: Global model data from CMIP3 ALL:anthropogenic and natural forcing ANT: anthropogenic forcing only Jones and Moberg, 2003: Hemispheric and large-scale surface air temperature variations. Journal of Climate Schneider et al. 2008: Global precipitation analysis products of the GPCC. Technical report, DWD Meehl et al. 2007: The WCRP CMIP3 multimodel dataset - a new era in climate change research. BAMS Temperature development in Northern Europe Bhend, 2009

Model response is too weak Model response is consistent with observed change No detection Detection using optimal fingerprinting Bhend, 2009

The check of consistency of recent and ongoing trends with predictions from dynamical (or other) models represents a kind of „attribution without detection“. This is in particular useful, when time series of insufficient length are available or the signal-to-noise level is too low. The idea is to estimate the driver-related change from a (series of) model scenarios (or predictions), and to compare this “expected change” with the recent trend. If change  expectation, then we may conclude that the recent change is not due to the suspected driver, at least not completely. Consistency analysis: attribution without detection

Consistency analysis Expected signals six simulations with regional coupled atmosphere-Baltic Sea regional climate model RCAO (Rossby-Center, Sweden) three simulations forced with HadCM3 global scenarios, three with ECHAM4 global scenarios; two simulation exposed to A2 emission scenario, two simulations exposed to B2 scenario; two simulations with present day GHG-levels; Regional climate change in the four scenarios relatively similar.

All seasons: RCAO-ECHAM B2 scenario Consistency analysis

Regional DJF precipitation

Regional JJA temperatures

Consistency analysis: Baltic Sea catchment 1.Consistency of the patterns of model “predictions” and recent trends in terms of temperature and precipitation is found in most seasons. 2.A major exception is precipitation in JJA and SON. 3.The observed trends in precipitation are stronger than the anthropogenic signal suggested by the models. 4.Possible causes: - scenarios inappropriate (false) - drivers other than CO 2 at work (industrial aerosols?) - natural variability much larger than signal (signal-to-noise ratio  ).

Overall summary How do we know that human influence is changing (regional) climate? -Statistical analysis of ongoing change with distribution of “naturally” occurring changes – detection, statistical proof. - ok for global and continental scale temperature. - In the 1990s, advanced statistical analysis needed, today also done with simpler methodology. - Consistency of continental temperature change with change in regions such as Baltic Sea catchment (temperature and related variables); problem with precipitation.

The purpose of BACC is to provide the scientific community and the public with an assessment of ongoing and future climate change in the Baltic Sea Basin. This is done by reviewing and assessing published scientific knowledge on climate change in the Basin. An important element is the comparison with the historical past (until about 1800) to provide a framework for the severity and unusualness of the change. The unique feature of BACC is the combination of evidence on climate change and related impacts on marine, freshwater and terrestrial ecosystems in the Baltic Sea Basin.

It is the first systematic scientific effort for assessing climate change in the Baltic Sea Basin. No additional or external funding was needed. The results have not been influenced by either political or special interests.

Past and current climate change  Air temperature increased by about 1.2 C since 1871 until  Most pronounced warming in spring.  Related observed changes in winter runoff, ice duration and snow.  More precipitation in the 2nd half of the 20th century with major regional variations.  No systematic change in windiness found.  No clear long-term trends in Baltic Sea salinity.

Baltic Sea basin land surface spring air temperature Past and current climate change: Air temperature WinterSpringSummer FallYear North1,171,950,78 1,041,3 South1,301,430, ,01 Linear temperature trends 1871 – 2004 for the northern (latitude > 60 °N) and southern (latitude < 60 °N) Baltic Sea basin.

Anomaly time series of annual precipitation over Sweden, (reference period ). Precipitation

Past and current climate change: Wind No changes in wind and storminess Number of low pressure systems (p< 980 hPa) in Stockholm and Lund

Changes in river ice cover duration (Volkhov river, Russia). Ice break up in Tornionjoki River, Finland. Past and current climate change: Precip and ice

salinity

Baltic Sea water level: Post-glacial uplift versus eustatic sea level rise, Stockholm Past and current climate change Sea level change Isostatic sea level change = land uplift due to post-glacial rebound Eustatic sea level = water level rise due to global effects

Ongoing changes in regional ecosystems  Associated changes in terrestrial ecosystems include - earlier spring phenological phase, - northward species shift, and - increased growth and vigour of vegetation.  Robust assessments of changes in marine ecosystems related to climate change are hardly possible at this time. Further research is needed to discriminate between climate change and other anthropogenic drivers such as over-fishing, euthrophication, air pollution and land use changes.

Past and current climate change: Terrestrial ecosystems Mean rate of change (days/year) of date of leaf unfolding in birch,

Marine Ecosystems: Regime shift in about 1988?

Caveats  Link to raising greenhouse gas concentrations is plausible, but no robust regional attribution has been established. (On the global scale this link has been established)  Many conclusions relate to different time periods studied, changes occur at different time scales: Variability versus trend problem.  Only few observational records span the entire recent 150 to 200 years.  Changing observational techniques influence data homogeneity.  “Detection and attribution” studies at the regional scale are urgently needed to determine the influence of anthropogenic factors in changing the regional climate.

Scenarios of future climate …  … constructed by feeding assumed emissions of greenhouse gases and aerosols into quasi-realistic models of the climate system.  Future emissions can not be predicted; only plausible and consistent visions of the future (i.e., scenarios) are possible.  Scenarios provide a frame for decision makers to explore the range of policy options to deal with the reality of anthropogenic climate change.  Scenarios are no predictions.

Scenarios of future climate change  Global climate models (GCMs) project warming over the Baltic Sea basin.  Regional scenarios are constructed from regional climate modelling, which provides more geographical detail and is broadly consistent with GCM projections.  Results from regional climate modelling do not fully reflect model and scenario uncertainties.  Within these limits, these results give an indication of plausible future changes by the end of the 21 st century.

Projections of future regional climate change  Increasing temperatures very likely during the entire 21st century, but size of the trend depends considerably on model.  Projected mean precipitation increases, largest increase in winter throughout the basin and decrease in summer in the southern basin.  No clear projection for wind speed and storms.

Regional climate model simulated precipitation changes in % for winter (DJF) between the periods 1961 ‑ 1990 and 2071 ‑ 2100 using the SRES ‑ A2 emissions scenario. The upper plots show results from the HIRHAM Model and the lower plots are from the RCAO Model. Plots on the left used GCM boundary conditions from HadAM3H; plots on the right used ECHAM4/OPYC3. BACC projections: Winter precipitation

Regional climate model simulated precipitation changes in % for summer (JJA) between the periods 1961 ‑ 1990 and 2071 ‑ 2100 using the SRES ‑ A2 emissions scenario. The upper plots show results from the HIRHAM Model and the lower plots are from the RCAO Model. Plots on the left used GCM boundary conditions from HadAM3H; plots on the right used ECHAM4/OPYC3. BACC projections: Summer precipitation

BACC projections: River runoff Change of river flow to Baltic Sea basins

BACC projections: Sea ice Mean number of ice days in a present day simulation (right) and two scenarios for (bottom)

Projections of future climate impacts on terrestrial ecosystems The expected future warming is associated to a possibly accelerated continuation of the present trends in - earlier spring phenological phases, - northward species shifts and - increased growth and vigour of vegetation changes in the relative cover of different vegetation types in Northern Europe

Projections of future climate impacts on marine ecosystems  No detailed, comprehensive analysis available –projections are more ad-hoc and uncertain.  Effect of other changing influences hardly predictable.  Possible Baltic Sea salinity decrease would have major effect on marine fauna.  Expected changes in precipitation and river runoff may have additional detrimental effects on the problem of eutrophication.

Past and current climate change Impacts on marine ecosystems … increase of temperature… Higher metabolic rates Impact on acclimation capacity Reduce the general fitness Reduce enzyme activities Shift in species composition (phytoplankton) Enhanced cyanobacteria blooms … reduction in sea ice… Ringed seal survival … decrease of salinity… Osmotic stress Shift in species composition (phyto– & zooplankton) Egg survival Food quality for fish (growth rate) Distribution of benthos Reduction of fitness Invading species

-a marked increase of mean surface air temperature of more than 0.7 C in the region during the recent century; - consistent changes in other variables such as extreme temperatures, increase of winter runoff, shorter ice seasons and reduced ice thickness on rivers and lakes in many areas; - a spatially non-uniform pattern of upward and downward trends in precipitation, which is difficult to be related to anthropogenic climate change; - evidence on increasing Baltic Sea SST only significant for the 3 recent decades, the century-long data records may have severe inhomogeneities; - assessment of indications that at least part of the recent warming in the Baltic Sea basin is related to the steadily increasing atmospheric concentrations of greenhouse gases; Major findings

- for the future, projections indicate that increased winter precipitation may emerge later in this century over the entire area, while summers may become drier in the southern part – but this expectation is uncertain for the time being; -for the Baltic Sea, a tendency towards lower salinity and less ice coverage could be expected; -no clear signals, whether for the past or for future scenarios, are available with regard to wind conditions; - observed changes in past temperature have been associated with consistent changes in terrestrial ecosystems, such as earlier spring phenological phases, northward species shifts and increased growth and vigour of vegetation, these changes are expected to continue and become more pronounced in the future; - an assessment for the marine ecosystem of the Baltic Sea is particularly difficult because of the presence of strong non-climatic stressors such as eutrophication, fishing, release of pollutants, related to human activities.

Springer Publication in January 2008: More than 30 contributing institutions More than 80 contributing authors from 13 countries More than 475 pages More than 2000 references (~150 non-English) Ch1: Introduction and summary Ch2: Past and current climate change Ch3: Projections of future climate change Ch4: Climate-related change in terrestrial and freshwater ecosystems Ch5: Climate-related change in marine ecosystems Ch6: Annexes

BACC and HELCOM HELCOM Thematic Assessment published May 2007 The report is based on the BACC material but condensed to 59 pages with a focus of the marine environment of the Baltic Sea. It has been approved by the HELCOM contracting governments of 9 countries and the European Commission. An unprecedented cooperation of a climate-related research program and an intergovernmental body

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