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Understanding Climate Change: Implications and Scenarios

Explore the main features of climate change, including global past data, numerical models, regional impacts, and probable conclusions. Delve into the discussion on natural fluctuations, extreme events, and the impact of greenhouse gas emissions on future temperatures. Discover the challenges and advancements in numerical modeling of weather and climate patterns.

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Understanding Climate Change: Implications and Scenarios

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  1. Glavne značajke klimatskih promjena i klimatskih scenarija Branko Grisogono Geofizičkiodsjek, PMF Zagreb Hvala: Č. Branković & I. Güttler, DHMZ

  2. OUTLINE • Data: Global Past, Present → Future • Clearing out the Denial of Climate Change • Numerical Meteorological & Climate Models  Simulators • Climate vs. Weather: expect ≠ get • More of Current Results: Present → Future • Regional Climate Change • Tentative Conclusions → Discussion

  3. OUTLINE • Data: Global Past, Present → Future • Clearing out the Denial of Climate Change • Numerical Meteorological & Climate Models  Simulators • Climate vs. Weather: expect ≠ get • More of Current Results: Present → Future • Regional Climate Change • Tentative Conclusions → Discussion

  4. 5th IPCC, late 2013  observed data

  5. 5th IPCC, late 2013 observed data, cont’d

  6. Climate last ~ 140 yr: economy ↔ 2 x [economy] ↔ 2 x [CO2, CH4, aerosol, land-use,…] → 2 x [warming,…] ≈> Global CO2 as a linear surrogate for all the anthropogenic forcings ≈2.33 oC /[2 x CO2] ↓ b) Residues ≈ Actual T- Anthropogenic T Mean Residues ≈ ±0.11oC  errors in 1-yr GCM hindcasts • Probability that it’s simply a giant natural fluctuation –how much rare? • Estimate the likelihood of a given amount of natural temp. change → bell curve  chance of a 1oC fluct. over 125 yr. as natural is ≈ 1:105 or 1:3∙106  1oC fluct. ↔ 5 σ • Nonlin. Geophys.→ extremes far stronger than from bell curve; maybe 100 x more likely → 1:1000; yet small enough to reject the possibility

  7. Meteorology, Oceanography, Glaciology, Vulcanology,… Climatology BASIC ISSUES: - MEASUREMENTS, WITH THEIR ERRORS, ARE SPARSE IN SPACE & TIME. THUS, INITIAL STATE OF THE ATMOSPHERE & OCEAN IS ONLY KNOWN PARTLY - NUMERICAL WEATHER PREDICTION (NWP) & CLIMATE MODELS CAN'T FULLY RESOLVE IN SPACE & TIME ALL THE RELEVANT PHYSICAL PROCESSES (ASIDE APPROXIMATIONS) - ATMOSPHERES & OCEANS ARE INHERENTLY CHAOTIC BESIDES STRONG DETERMINISTIC COMPONENTS

  8. Numerical modeling of weather & climate - The atmosphere’s behavior is governed by a set of physical conservation laws telling how the air moves (Newton’s laws of motion), about heating-cooling (laws of thermodynamics), roles of moisture… - The governing Partial Differential Equations (PDE’s) can’t be solved analytically – only numerical (i.e., approximate) solutions are possible * Given the current state, I.C. & B.C., the equations may be used to pass the info forward in time  forecast * The observations give the sparse current state – an incomplete picture, the weather & climate models may process the obs. → a fuller picture of reality

  9. Unresolved Processes  Parameterized Parameterized: Radiation, Moist Processes, Turbulence,…

  10. Predictability, like Turbulence, is Flow Dependent

  11. Weather vs. Climate Models • Resolution & integration length of the governing PDE’s (motion, mass, thermodynamics, spicies) • Parameterizations (different space - & time-scales) e.g., weather models might have slight drift (may avoid some feedbacks, etc.) …climate modes not! • Sometimes numerics, due to numerous couplings (feedbacks) being modeled differently, etc. • Oceans, soil, biosphere, ice,… treated differently

  12. 5th IPCC, late 2013 - numerical simulations

  13. Is the extreme weather we see today really caused by global warming? - Claims are made that push beyond what science can tell us. Attributing cause ↔ effect to individual weather events is fiendishly difficult. Climate is about patterns, statistical behavior…

  14. Temp. at 2m,Summer(2011-2040) - (1961-90) reanalysis obs. HadGEM1 EH5OM GFDLCM21 RegCM srednjak ansambla

  15. Precipitation, winter(2011-2040) - (1961-90) reanalysis obs. EH5OM Ensemble mean RegCM GFDLCM21 HadGEM1

  16. ExtremeEvents No. Wintertime Cold Days No. Summertime Warm Days

  17. HAIL EPISODES IN CROATIA Courtesy of Damir Počakal, DHMZ Average duration of hail episodes: continental Croatia: 4.3 min in 1981-2015, red & 6.3 min over the Polygon (NW Croatia), 2002-2015, blue. -There is a +trend in the mean duration of hailstone events in cont. Croatia, Počakal (2012) -But typical, standard climate models don't have those variables included yet - should be there!

  18. TENTATIVE CONCLUSIONS • Current surface temp. upward trends & future predictions are unusual, unexplained by natural internal climate variability at ≈ 99 % confidence • “Predictions” based on greenhouse gas emission scenarios & links the gases concentrations  temp. via modeling & simulations • Included: population growth, economic development, technol. change, social interactions • Besides large spatio-temporal variations, global warming is real, it exceeds in magnitude & pace natural changes over more than the last 103 yr. • No detailed clim. projections for hail-storms and wind fields in/around Croatia

  19. TENTATIVE CONCLUSIONS cont’d • Immediate changes seem needed: lowering emissions of green-house gasses to meet the scenario with global ~ +2oC in ≈ yr. 2100 • Targeting and promoting more human technologies, renewable energies, healthier food & water  Humanistic approach needed! • It is wrong to deploy “instrumental rationalism” (max. efficiency only) & blind pragmatism based on e.g., large resources & markets • Make reliable regional climate – economy projections & space-time variability for next few decades (agronomy, energy, tourism, education, traffic, etc.)

  20. The End

  21. IPCC 2007 Model Simulations

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