The focus inPapers I and IIIis narrower, both in relation to their spatial domain and the types of applications motivating the analysis. However, this does not necessarily limit the number of potential end user groups (as defined in Chapter 3.2.1) ofPaper I, as the climate information in these papers is still somewhat general and can be used in any application which is sensitive to them. The implications of climate projections for the specific applications in the papers are also presented. Although publishing these results in the literature alone does not correspond to being an ”honest broker”, this information could be promoted in such a way in other contexts as they have some implications for adaptation over these sectors. This is possible because vulnerability and exposure components (Fig. 1) are also, to some extent, taken into account in these papers by including a process-based point-of-view for the impacts. These impacts, rather than climate change alone, serve as the primary reason for adaptation in these studies. Climate models in these studies are applied using both top-down (Paper I) and bottom-up oriented (Paper III) approaches.
Table 2 (from Paper I) shows the growing season precipitation climatology in two regions of Finland for three observational products and ENSEMBLES climate models in present-day climate. The maximum of the growing season precipitation occurs in August for both study regions, whereas the crop productivity of most Finnish cultivars can suffer from water shortages during the early part of the growing season (May-June). Sufficient water availability during these months is crucial for crop yields.
Climate models typically simulate too much precipitation, as MMM values are larger than observations in almost all cases. Removing this bias from the simulations prior to calculating future precipitation was done here using the delta change method (R¨aty et al., 2014). Also the choice of the precipitation product which is used to correct the bias has a marked influence. The FMI grid product has the highest information content out of the three products, both regards to the number of precipitation stations over both areas and the used resolution. For CRU, the information content is the lowest. Here, this translates to higher estimates of precipitation over the study area for FMI grid, and lower for CRU. Figure 12 presents the projected changes in growing season precipitation climatology as simulated by the climate models. Except for August, projected mean precipitation increase is statistically significant on a 2,5 % risk level. Precipitation shortage during the critical months of May and June is expected to become less severe on average.
Table 2: Growing season mean precipitation values (units in mm) for the years 1961-2000 over two study regions over Finland. FMI grid, E-OBS and CRU are gridded precipitation products, MMM the multi-model mean and std the inter-model
standard deviation. Table from Paper I.
Month FMI grid E-OBS CRU MMM std
NE region
May 43.7 39.4 35.7 64.6 15.9
June 64.7 58.7 56.5 77.3 16.5
July 72.7 67.7 63.5 89.7 22.2
August 87.3 79.3 72.6 94.6 23.2
September 66.2 59.8 54.6 87.8 18.6 MJJAS 334.5 304.8 282.8 414.0 86.0
SW region
May 34.9 33.3 33.5 59.8 11.3
June 52.5 50.2 46.8 66.8 15.5
July 74.5 74.5 71.4 74.2 17.6
August 78.1 77.5 75.8 78.4 18.7
September 61.3 61.0 61.3 75.7 13.9 MJJAS 301.4 296.6 288.6 354.9 67.1
The conclusions ofPaper Ialone, however, are inadequate in providing all the needed information even for the climate component of adaptation. The increase in average precipitation conditions does not take into account the inter-annual variability (see Fig.
3 in the Paper) and also evaporative losses affecting total water budget are expected to increase in warmer climate conditions. These processes affecting the vulnerability of agricultural applications were not analysed in depth, as the primary purpose of the paper was to analyse different observational precipitation data sets and projected precipitation changes of the different RCMs. As projected precipitation estimates provided in the paper are generally applicable and well documented, they could also be used for other purposes as, for example, to estimate flood conditions. For flood applications and many others as well, several other information sources are likely to be needed in parallel to the results presented in the paper. The findings of the paper do, however, have the potential to influence further crop breeding, which is a long-term excercise (Forsius et al., 2013).
Figure 12: Growing season MMM precipitation changes from 1961-2000 (FMI grid observations, blue) to 2061-2100 (climate projections, red) over two regions in Finland, as simulated by 13 ENSEMBLES RCMs. The error bars around the MMM
line show the standard deviation of inter-model spread for each individual month.
Figure from Paper I.
The results of Paper III are considerably less general, as the key climate variables affecting road conditions are application-specific and might have little relevance for users in other societal sectors. The main focus of the paper was to apply a bottom-up approach by using a process-based road model and to assess the sensitivity of it to cli-matic variations of temperature and precipitation. This was complemented by using a top-down approach and providing estimated impacts of road network to the projected conditions of other key climate variables. As was found out in the numerical analyses, condition of the road surface layer seems to be a considerably more important factor in defining proper water runoff treatment as compared to the actual distribution of precipitation events. Typical high-traffic roads are very effective in draining the sur-face runoff water even from the most severe precipitation events, whereas those roads with heavy cracks in them are unable to drain runoff water fast enough. As a result, water is able to penetrate into the road sub-base layer and may thus deteriorate the road structure. As the maintenance life time of most roads (in the order of 20 years) is
considerably smaller compared to climatic time scales, bottom-up process understand-ing of the road structure and properties constitutes a much more important factor for efficient adaptation as compared to being able to accurately estimate the projected climatic changes within this time period. This conclusion outweighs the sensitivity of the projected climate model results to various factors and emphasizes concentrating on the vulnerability component of adaptation in this specific application. Climate change was estimated as also being able to indirectly affect the exposure component, as the movement of people will alter the road traffic volumes and maintenance strategies in different geographical areas. In the conclusions of the paper, iterative risk management and application of existing practices from areas with current climate conditions similar to those projected, were also highlighted as suitable adaptation strategies. In all, the prospect for facilitating adaptation problems in Papers I and III by focusing solely on the reduction of epistemic uncertainty in climate change projections seems unlikely.
5 Discussion and conclusions
In this thesis, widely used climate model data were both applied in impact studies and analysed focusing on previously unstudied aspects. Both best-estimate and probabilis-tic future climate projections were analysed. The findings of this dissertation give rise to two main conclusions:
1. If multi-model ensembles are assessed from a purely statistical viewpoint using traditional analysis methods (”one model - one vote”), the derived climate pro-jections are unlikely to be substantially changed through the development of the climate models themselves. This is caused both by structural differences between climate models and by chaotic behaviour of the climate system.
(a) For most parts of the world, multi-model mean projections are statistically indistinguishable across several model generations. The user is able to see hardly any significant differences between them as the mutual ordering of individual model projections inside the uncertainty cloud varies between consecutive model generations. Model-dependent component of model de-velopment is considerably larger than the collective component shared by each of the models.
(b) By using in-sample variance as a measure of uncertainty, probabilistic RCP-projections acquired from CMIP5 have a larger uncertainty compared to the SRES-projections of CMIP3, both for modelling and scenario components.
If these simulations are used for adaptation, optimization of different appli-cations to climate correspondingly becomes harder. In case the application is highly sensitive to climate, postponing adaptation decisions in the hope of having more narrow uncertainty intervals at disposal in the future is judged as a highly unwise strategy. This, however, might depend on the scale of the application and the climate variable of interest.
Due to persistent model-specific differences, physical model evaluation should be incorporated whenever physically understandable and statistically robust cause-effect relationships are identified. At local scale there might remain more poten-tial to improve projections through process understanding. Finding universally applicable constraints, however, is harder if the model simulations are analysed
in a general manner without a spefic application in mind. Physical model in-terpretation can possibly allow more confidence to be attached to multi-model results, as purely statistical approaches suffer from several limitations.
2. Subjective interpretation of the climate projections is often necessary, as the used data set and applied methods might be ambiguous. This, together with the spe-cific information demands of several applications, encourages climate services to act as ”honest brokers” whenever tailor-made estimates from future climate are needed. Adaptation requires interplay between the user and climate communities as the prior knowledge on the importance of the vulnerability (climate) compo-nent might be unknown for climate modellers (application users). Comprehensive adaptation assessments for specific application typically require information from both components, the relative importance of which can vary substantially. Top-down and bottom-up approaches can be used in parallel in many assessments.
Adaptation to climate change seems unavoidable, because of the long time scales related to any mitigation efforts. The utility of future climate simulations depends on the time scale of the application and whether it is sufficiently long to be affected by climate change. The information provided by the climate models can be accommodated to adaptation assessments using several approaches, either using generally applicable and conservative methods (Papers I and II) or by using application-specific quantities and incorporating these with detailed process understanding of the application (Paper III). A generally applicable approach allows the data to be easily used in several societal applications, but is unlikely able alone to provide sufficient information for any of them.
On the other hand, directly engaging with applications allows the provision of sufficient and contextually relevant climate information.
Emphasis on the scientific uncertainties alone is unlikely to encourage people to make adaptation assessments, but their proper acknowledgement is necessary to guide the available resources in an efficient manner. In adaptation problems, natural scientific part typically needs to be incorporated with the sensitivity assessment of the system for climatic constraints (Paper III). The gap between end-user needs and the ability of climate models to provide the required information will remain fundamental for several years to come, which allows subjective interpretation of the results. Climate modelling community should not advocate specific policy, but on the contrary: it needs to actively engage with the user interface and promote good application-specific communication approaches.
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