Climate change in the Mediterranean area in Apulia P.Lionello*, L.Congedi, M.Reale, L.Scarascia, A.Tanzarella * Univ. Salento and CMCC

CLIMATE CONDITIONS IN THE DECADE CLIMATE CONDITIONS IN THE DECADE CROPS PRODUCTIVITY, ENERGY DEMAND, WATER DEMAND, ECONOMIC SECTORS IN DYNAMICS OF THE CLIMATE SYSTEM DYNAMICS OF SOCIETIES AND ECOSYSTEMS GHG CONCENTRATION, AEROSOLS, LANDUSE

PRESENT CLIMATE FUTURE CLIMATE EFFECTS OF CLIMATE CHANGE ON SOCIETIES AND ECOSYSTEMS DYNAMICS OF THE CLIMATE SYSTEM DYNAMICS OF SOCIETIES AND ECOSYSTEMS ANTHROPIC FACTORS

PRESENT CLIMATE ANTHROPIC FACTORS FUTURE CLIMATE EFFECTS OF CLIMATE CHANGE ON SOCIETIES AND ECOSYSTEMS DYNAMICS OF THE CLIMATE SYSTEM DYNAMICS OF SOCIETIES AND ECOSYSTEMS

PRESENT CLIMATE ANTHROPIC FACTORS FUTURE CLIMATE EFFECTS OF CLIMATE CHANGE ON SOCIETIES AND ECOSYSTEMS DYNAMICS OF THE CLIMATE SYSTEM DYNAMICS OF SOCIETIES AND ECOSYSTEMS Known with acceptable prognostic confidence on multi-decadal time scale NOT known with acceptable prognostic confidence on multi-decadal time scale

PRESENT CLIMATE ANTHROPIC FACTORS FUTURE CLIMATE EFFECTS OF CLIMATE CHANGE ON SOCIETIES AND ECOSYSTEMS DYNAMICS OF THE CLIMATE SYSTEM DYNAMICS OF SOCIETIES AND ECOSYSTEMS STRONGLY nonlinear processes (limited predictability) ?

ESTIMATE OF PRESENT CLIMATE CONDITIONS FUTURE CLIMATE PROJECTIONS SENSITIVITY OF CROPS PRODUCTIVITY, ENERGY DEMAND, WATER DEMAND, ECONOMIC SECTORS TO CLIMATE CHANGE DYNAMICS OF THE CLIMATE SYSTEM DYNAMICS OF SOCIETIES AND ECOSYSTEMS EMISSION SCENARIOS

ESTIMATE OF CLIMATE CONDITIONS IN APULIA CLIMATE PROJECTIONS IN APULIA SENSITIVITY OF CROPS (OLIVES, GRAPEWINE,WHEAT) TO CLIMATE CHANGE DYNAMICS OF THE CLIMATE SYSTEM DYNAMICS OF SOCIETIES AND ECOSYSTEMS EMISSION SCENARIOS

Lionello, P., Gacic, M., Gomis, D., Garcia-Herrera, R., Giorgi, F., Planton, S., Trigo, R., (...), Xoplaki, E. (2012) Program focuses on climate of the Mediterranean region Eos Trans. AGU 93: nd MedCLIVAR book: THE CLIMATE OF THE MEDITERRANEAN REGION: FROM THE PAST TO THE FUTURE Region/isbn /

Figure 1: Köppen climate types in the Mediterranean region: subtropical Steppe (BSh), midlatitude steppe (BSk), subtropical desert (BWh), midlatitude desert (BWk), Mediterranean with hot/warm summer (Csa/b), Humid subtropical with no dry season (Cfa), Maritime temperate (Cfb), Humid continental with and hot/warm summer (Dfa/b), continental with dry hot/warm summer (Dsa/b), Tundra (ET). This figure is based on the CRU temperature and precipitation gridded data (New et al.2000)

Temperature trend o C/decade Observed climate trends

A1B scenario,: Seasonal (DJF, MAM, JJA, SON) map of temperature (K) climate change (difference minus ) from an ensemble of GCMs. (adapted from Giorgi and Lionello, 2008). temperature: Climate change

A1B scenario: Seasonal (DJF, MAM, JJA, SON) map seasonal precipitation (%) climate change (difference minus ) from an ensemble of GCMs. (adapted from Giorgi and Lionello, 2008). precipitation: Climate change

MGME ensemble average, A1B scenario from Giorgi and Lionello, 2007

Annual average T max (a) and T min (b) for 11 individual Apulia stations (grey lines) and the average of the 11 stations in red (T max ) and blue (T min ). Linear trends are shown for the whole period and (black lines)

Trends of monthly mean T max (panels a and c) and T min (panels b and d) over Apulia ( o C/year). Trends have been computed for the whole period (panels a and b) and for the period (panels c and d). Circles with increasing size denote trends statistically significant at the 90%, 95%, 99%, 99.9% levels. Vertical bars show the 1 to 99 percentile uncertainty range.

Total annual rainfall (mm) in Apulia for the period The black line shows the overall regional mean (with its trend) and the colored lines correspond to the five areas in Figure 1b.

Trends of monthly rainfall (mm/year) for the whole period Statistical significance does not reach the 90% confidence level for any month. Vertical bars show the 1 to 99 percentile uncertainty range as computed by the Mann-Kendall test statistics.

Linear regression between must and wine production (thousands of hl) and average seasonal temperature T m (°C) in winter (a), spring (b) and summer (c) and total amount precipitation RR (mm) in spring (d) in Apulia for the period Correlation with seasonal temperatures is significant at a confidence level larger than 90%. Correlation with spring RR is not significant.

Observed annual (black line) and estimated values (LRM, red line) in Apulia for must and wine (a, units =10 3 hl), harvested olives (b, units =10 3 tons), harvested wheat (c, units =10 3 tons)

Parameters of selected LRM ± standard error Wine (10 3 hl)Olives (10 3 tons)Wheat (10 3 tons) Intercept46400± ± ± 110 coefficient R 2 coefficientR2R2 R2R2 T m summer ( o C)-1520 ± **** T m winter ( o C) -109±950.13* RR spring (mm) 2.9± *1.52 ± * RR summer (mm) 2.3± * Overall R adj ****0.20*0.09* LRM coefficients, intercept, coefficient of determination R 2 and adjusted overall R 2. Only seasonal values of mean temperature T m and precipitation RR with statistically significant correlation at the 90% confidence level and used in the LRM are reported in this table. For olives, “RR spring” refers to the previous calendar year. Units of coefficients: for T m versus wine 10 3 hl/ o C, versus olive oil and wheat 10 3 tons/ o C, for RR versus olives and wheat 10 3 tons/mm). R 2 and R adj 2 values are denoted with ‘*’, ‘**’, ‘***’, ‘****’ for 90%, 95%, 99%, 99.9% significance level, respectively

Time series of annual (a) average (b) daily maximum (c) daily minimum temperature (°C), and (d) total annual precipitation (mm) for Apulia. Values are anomalies with respect to the mean baseline period Lines show the CIRCE climate models: ENEA (pink), INGV (blue), MPI (green). The grey area is delimited by maximum and minimum values produced by the set of models used in the ENSEMBLES project and the red line represents their mean. The thick black line shows the time series of the observations

Trends of annual average temperature (a), maximum daily temperature (b), minimum daily temperature (c; °C/year), and total annual precipitation (d; mm/year) for Apulia. Values have been separately computed for the (left bar of each pair) and (right bar in each pair) periods. Bars show the CIRCE models and the mean of ENSEMBLES project simulations (red). Colors denote the level of statistical significance of trends (not significant, 90%, 95%, 99%, and 99.9%. The Mann-Kendall test has been used). In panels b, c, d the single bar on the right shows the observed trend in the period

Crop yield: wine (a, in thousand hl), harvested olives (b) and wheat (c, in thousand tons), simulated by the MLR model using climate data from three different CIRCE models for the period ; ENEA (green), INGV (violet), MPI (red). In all panels, values are anomalies with respect to the mean. INGVMPIENEAENSEMBLES must and wine-20%-24%-20%-26% olives-19%-17%-8% wheat-4%-3%-1%-2% percent variation of wine, olive oil and wheat production between the period and Negative values denote that future production will be lower. The first three columns are based on the results of the CIRCE project models, the fourth column on the mean of the ENSEMBLES project models

Apulia meteorological stations shows trends towards warmer and marginally drier conditions during the second half of the 20th century. (Annual T min has warmed 0.18°C/decade. during the decades annual T min has been increasing at 0.45°C/decade and T max at +0.47°C/decade, with a faster rate of increase during the summer. The detected decline in total annual RR-14.9mm/year and not significant for the period ). Climate model projections suggest warmer and drier conditions over the next few decades. (During the second half of the 21st century average annual, minimum and maximum daily temperatures are projected to significantly increase with rates in the range from 0.35 to 0.6°C/decade and precipitation to decrease at a rate larger than 10mm/decade). The records of wine production, wheat and olive harvest present a statistically significant link to seasonal temperature and precipitation (seasonal variables explain a significant proportion of inter-annual variability: 40%, 18%, 9% for wine, harvested olives and wheat, respectively). Results suggest that wine production (-20÷-26%) and harvested olives (-8% ÷-19%) could be impacted in a negative way by the drier and hotter conditions characterizing Apulia in the first half of 21st century production, while harvested wheat (-4% ÷-1%) will be only marginally affected. This study does not actually aim at having a predictive skill, important factors and adaptation have been neglected, but to show likely future scenarios and potential critical sensitivity. Conclusions