Modelos climáticos precisos desempenham um papel crítico na ciência e na política climática, ajudando a informar os formuladores de políticas e decisões em todo o mundo enquanto consideram maneiras de retardar os efeitos mortais de um planeta...
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lZQStWs6VQm1VLUfC8IPusz4dwXiBvwNe5S19rPMBYofi4rOz/6rTZ1Wt2G0sw3/GL4aJKgtLBxKzFzlH0/yv1BUtHKlr+Fke09V4Z6KYA6qS8Iw8Y036dpE68kMT8caVWG1a4Eb4JR7RtNRhhVcFG7pRYmWp9oKNmy1wN4YkLK1Gd2p4o2lkMI3yh9eyPWV9RjInmsLmlFr6EKYa5EgR0WEGb/ABuY06wmizc9wPLbOK0a1YJ+tzLe7GXVeILMSVWarsiB1KoyfdZ2EdbtCi6WwOJUWOIjgs9gHcwmtIex54toqFh91P8AE2VOMSgRorZhoDoI1WFtI3KCFHeQdFja2qpZt+FRjfi+HAFTH5Xg/K7GPcrVnyjMDojJwwd4XmMU4GPTg9woYgoPZRsfpWS2k/yrI9rT1VLVtoP6lWLqkXUcNngiafKpZZVJEKTZhUYAoVbJvwsrA3oi6Biux2gI+nVT9ExyWPAGousmYucSvqWri2ukI+Et5FBltZ03OIosQOY6BpvGGKfi+XGF41hsRh5qr3fN3jAWkdUTibPBFp1G3Jo1Q0XRjrdRYi2RyWdj/hY2tFm2PlCyL4j+VeEOPE36X5YX1bR2J/xCnf3sehyOI6LO2TxlDBQ7kMds+d9VhEwON2a6jSFrKoVEqt1FqquCpPwqtj3uyxPNTFxbZ5jx3IfUdiHBBhIs+LolRk/vIhdi+P8AJYpxciZWG0YWHfCiloDTgUYEC+YkbwshuNiB1O1Ryh1odrKgXOkqGokrD4WlUFeKlQSJWcS3osopdrdRQmqv4U7eu1F2t2q1v12M7g3qsjgVgbWN99bQTyqiMwduMbFWh3VRlK3/AAsTd/FQ2tnvI3rCxoaFleq4T7KLZuHmFAMcOaId7LVDFpcZAVAsoF2qqiyzoSix2o2CBaSdY4XQIBU2ohqx2Yyf62MriOi1nqiTqe9h5hpWUz0QLd6zinBS1sINL2A9EO1MdEC+sKNy31v1C0WiELRVXBarWbtVl2dbsxVFpdpsQ0Y15f5VGU6rQyv1PK7VxaD+kItsGhg/t1XjjooLzHLbGJwAXhLzzWY04KHOJA2Y3IMfVVChQ8e6yqqpN2LeuN0loJ5hZcp5KjmrARNYX1X5eV+q1CmzzN7+qnYmq0lVaaGsrF8LVcbziCmoWpPK6RqsvvdVaLS8LOD7LE0SFRhJ5haXuAFG/wC1G9Ruu3KkFVUhpcv6eA2Q4anUrO4N4SVjIAPcR3f07Q04qWnEFpRYmaoG6rVIohDiHTTZmziTqmh8SFAXFQqLE4wmFo19Bl2N6FYQrKrNeSIVdLpduWul8KNqq4hGIWtFR34WJwRqHNR/S0cFABgKGNRFZVTRaLwqB7pgAgI4wGjirbA2uw4WnGnNS8+kkIlpgrEXQee+6g2KrE0kzuWJ1FibZ0O8qWrhfSFmLnfhR8FTbvgDisTnAwo77jfRHEVk0WqlpUOoOih0h3JCKrIYHBEv/CxFwwQq2jVH/m6JF1VlIumbqiFloo16rC7VU1UOKw2VFmd+FDQhwWGKqkSVosRygqhxO3LE+SnvaKz6sAVQl+Y7l4oVCocVTVArEHSCgBmPBTSuioSXHgFjcRh3hYQ4ckct0UHMqAzEpPohj0VH/F0g1VbpJu53aH2UQoWOpKxSZVV/+qpQLXIE6j9KzWceyBG5S7EFRwXha7/JQ4EKcS3fKoVWvVeFSXQFiQqgpci7TkqrE0wZ04qH0VB6iqDkQR8qAFBCoLpiVljEjLjO9BpBhcDwRl7jy0VXGFX0lVAvrVS0qDW+phZTKkflZnwP6Sjo9o4hYm0UXSLzisy47lifZhhUtLnHmaLRVK0UzVVEqLtFpfE3VWtwRb9Qqp9VXwlQCaLMsRUgxy7mfRVUyuK0Wl1VA2BJ1UDCUMILY5qH6ckDu0dxWQGAUJBC4LjdIiUcWnFaqt2sKlbohSJg7U4oU30WK0in6USfWRCotEO4p6OihTecKq031F4CpW7msG8rDeMSIiFhYYC7TMsTatUbGqglTdpeXTdT7DDtD4drX0+m3J15IkmVUArwkdFQqRv2IcarSvHYpfl04KshUuqsId8ogquzX7GGRBUO1UephZhL1psUF+XXZBGixHRUu5hBrxSEIaY4lU/2qoxs0cV+n4RqPhUVdqPsk71zuOzqhNPQ/Ud7X1voqqN6quWwQV9N2xicFhAoFO66Qqbc7FFUrKuf2al9djSVwWvflvBVvpfF9diUHKuo2JcVJvop7mFr+wS7itdmD3JHFCd90kqLNS47MXR3c/aqqno49BVQBBVQ4qHd9p9whTN9b6et0qp4Iu47fNTtx930VPQVPd0WAe/cx+wAR6qG+in7xCp6atxA9LH21w37u9j0cKno6bA+2h3dz9qqtLq7Vfs2Hj3cd9i4bQ2D6bS6ftXafKymh7k98I37dbpKp6WTfBVFH2lwVPTYD7KNk+ok126/aHbNLqehB7ifRn7j19QOI9XHH7iNmvo52IHqJv8AEqOWq1up+waiq09XouF8GvBRv+26/d5VVr9qw0/cM3FoH7Bqob9gdXeKXYQPf7Nr98ICi/T9wxT47mh/5BnCVLw1g/qKjFJ6fuACY5qceJiwsEIh+g09ZABKpZn3RxQ2Pys2I+67MkFUwn3Xl/kLynLO0i6ih9k7Dx0hVtDC7NxB5rzfwjizzxCn6aPZNqiWuLeS7R8jksstPGV2dp8o/UeCOSzPdPJdm8RzWrp6rDXqstr8hUtGqfEeDVhc0g+v0OxWiOaDum4FzH/+EBgAjgsLGgC5zHnKDQd7DGyquaDwVbUfCm0dj5LLaOC838LzXKrnEcF+odCvMcvN/CzWjlGD3X0ywEc1TEPdQ1oHcZmg9ViwRyCljK8fS6BeU1dmSxUtGqgDuiztI73QrELMwvCPlfo+V2jwOio5wKpa/hea34WW0au0NOLVQBwWB1lAO+LpewE81SyZ8KtmPZeH5RLq9FhLJ6oOaC2OBvlFzrMt3adzQSpo0f1KbXOfwslm0e33KXLJZiOaxu1v8v8AK8teUV5Ll5TlUAdSvG5TjcRwXlNWVgHqc1kPaioXBZLX5C/T8rxtWrPldpaR0VZd1XZsA+6ZUbRrtdxVbSD0XnfhUtR8KmF3v/7Lf//EACsQAQACAgEDAgYBBQEAAAAAAAEAESExQVFhcRCBIDBAUJGhsWDB0eHw8f/aAAgBAQABPyH+vxOhhuHwTLWTyiOzhDY/EbT8QI1EFbzxKjb8kRh/Dl/RwlQXxNoHkig2ow4Ow2Y9APJc/iclFxa4bbchkfuPh10pmHF94dm/JG1P2BjYO7ANn+JlsvdAWaj+aCCmm8EDRHZzA2p6mJqXkXAGy3xKSxx4UA2vuj8T/jmIatXKU1avlQQJ6qUEdrsl3LRK+HFeDxB0ddB+B6jcHtg8ZvA7BlMplMpgFyqJ0ntKUAxLz9aF7H1C7lhx+K2/cr1RBTDEr07Erb5dD+JW7nGo/uOj2lSdMutfZWLYw/2Mr9RVHBB+zpDKErN7r+YsKrkxb9SoV0EqnvH72cgqYyuACk8MohsFtT9RjwiggnWVxX7xmCy9sf8AaF6VPIf2h6tDouf1KsE6JiYp7LfuZoV6pmWv3wj8fb5LE2ah235mA106TJWGIsv4BpsjQZcqR0rJjk95katwxUyxszWP4lsU6jbv0uXLJj11WdSOsfmoIvtQL+pcChycy+x6Ge81CLkhG6Se0Igp0FkbwjkfosGU8aeIlavFH+HonJUjwbPhqBdWy65dcCJo06o7SPaViV8h7RPErx6UfLdu4aNSmwOf94LKHOWH+JfepW/yj1I3QEWzsWikfv1BYYadwklzdSA46txYZgxb/E5i9tSlZ+XP8TEf6gk8PcWJ2hwnwHFhpJcW978lFNHZefxiPgQyObv81FLbz8rHSEY6emOY/B7vi6JTWaSLl6p0N/zHrc7vpXNLLDzM2p3ptc8tagpa8LL4mZ7f/tmwbqQJ6ZmwxSv66Mp5liXFTjZKieN7r8QOzDGPCEu3glkSVU8lxs6zIq9cRdOHQLZSZrYfyodK1tWzEF3UkudSMor0Fsw4Q8qQdWzGR9rpfoNYmgnWKIS2/WEBs93KGNZNN6lPAe4TiTH8gonSPvuewoWZQ17GYFlBwcJGESPPEPILrpqwPjgrv7j5RyEj8sPgflgBfGkHXhFFXli0B94rJ5MoaDIVHtLDolHJEIJ4v+kqGd7G4AHYNxpMnVLjKjeqiOknqlerh1gk0B8On8QyfBxuOp5cZZhf4r4B7fGzRHBYIWHXtOkbxiJd6KxeGGik/wC+IZcHqCPV4t6JlpHWoUw6CM7qDCyGiq0DZOKIf+jDr8Iv8S3X0qqDUVm5dCrfXuuARVDCvB7zJ10viAB0RpdmIjSZ6yurLEp1jJRz6XfC8oQRXETaDlsINdvcsCRGytPTF3WesDpnciW/wYS4/naEmgOYNOPB/wCIETo8ljoropyPB+yY7Q73E43IyXFoG9yX2JwozOw0XRhI5FrXhi6du2YrhJjpe8MxZurZt0O2YA0ssRs6YrxcXX8pmQDlpCpa9Vv96lRdPF3DSfVgpVNqimFgH43Ls2LdYd6QuaK9piZihRbKh1GCwzKloE9uZdffCpYke2u5TtVocnvGpW5CHVELsh9bdXJYYjT0qAHdjji10Jcx1Pc2Yjc6huAZb4wyShBI1sPjtklybZus77le46S9hcsg3COsL5nWWZbonhKMBdNmfwIJfmzyO9wCBPfZxfxOAAQsxGDvK8y3B22zIPjxTwmHXy9LfBAHjLANYnnEMA6yie8WcQDaUZeSGoH0GoyivRqeJd8+i6l+lKuyvRy1+6oPlW3hYSrI8pek0wNpUP1C3NQX17RK5eJgRqZ5DyMaMwOQ951MnWBE9qPMvkUYFL3HEvxt1RCrXkZs4b944o5PwjhxWdWNH+88eWVMoM21+odPmf4Jdbu4tS+V7pLkqzFvNQI0ahpmErOH9h3GnKzAeu1H7sNPtdJcRh3U+FVY6iQsvrxMCPtj1cPq7rRtWdZO1iFbh7ROJ7hrYUvMpG/rZZgcpgkWhxcvlg52lrzOFsne4jXBf9Q2I7RcU2AlEDWbg2BTlEx4sOOhMjd3B7n4iTA5mTZFsSu0KWzmA5inDDozOk1LuYusZY8AgDv0Gmc+ZTqs7aWtJBwzmkcE1om4QJTq1S+qTaOov1lpceght6VWSCYF5HoNS7gp/LIDrQUKcSzF6bcIQpDK8MdZqsHjeEc13fWXNldHMxwXSMTF7U2j8nEU3CQj0U7cS6vAkwXVywtDuXMBxh0Ipgnd3PB+ZVF7MU8xRbX1ik2utYgs18AEozaS5NxdubtlbZuaTPiKzF8fBaVt6qljRvLh2lvyhro1HHp1uRGJVbbNs6l7yINNrZfEWTWt2bq8qJCwbN/+S40wxW47BVdKMEsNV5jdx5hXfxRQmUXhSuO0GpYRxihDFg7MXkwNd5cx+06gO8vYwzbj8Ruy/OZfBntLmLr3mjcTcu+gLwO0c3au8p2oWaBYxt9JQzD2l/lN00QPLmkr6rjBGNs4jlXgYiuspt+YIOmmr3Sizp4QhXCnS/Pxb1NxF3t/Euk4y9BMHhaGAgLQAv4eYbXG7DjzGnRATisM6hxac2nnmZwNdmGwA9yLvVv7lwy4CxFFIrrfsREacWyYY72k32O+IKlMsLq/eW8OJe5evjp0icFmQvDrG4sO+Y6oeSmIpHZ81FxHxpHcM9vWmQZlxFsSijXYmuaEK2rMLpLC40xGkls1VYiZBDpMG92lKFRNuJcgmUPJlBb1y4lcQmIpK9EU3GEp/EE3gmEUE0JLuDNZ4jGznI/xDCYESZ4Y6sNb41FOmRevRKVk8IdD6jEVwxM27EUpwdM+0S1rglCyUvPR5gKLWDfwDPv6niZoHjsiBQ3Uo0j8AHLXtOW+3plBoSkMX8q9jeTmXGV5y8Z1BuWmHsgAlfozzTrFtgx1mWptNEXShKvcKymSDXVjrsTQM3FTLmAysswaQrsEMtEJsaTUcHdK0LYzzEVBtkpT5tZywSypYNkFcPMAZ3LNEELCUAMeAnjHqtsXcJpLtLwr3iEqdWIjTohWkpBissy6UJ0MyNeIGYERgcTtlJXeal4xmXRpg/cvilDBULwHnkhPHhgxCyhfBpH3sUJUR3BJRFalr/cp2f8AVwN55XiABk2xB7jcuSBkSs9JwoJsi9Z3AANsrY99HwX4lWJuMg1axr4ELYV2mOvz700xzqlWn2hRtjygBDHOxh1BeZq9m5U55i5KY5YzXRm2CIVN/eZ2OCxLjPYbuOqcu8o2VZgdLY4VkeXUJWp2aEHZ45KSEW4as3Er1UzL/U2ePm7ZaI06IAYx7RrglNsd6xAKpDmYDAaqIyXnrLpLMwzVbXLMfBoNKgbqhmnBKrQbFx5ugKczp1keOkvDDY3xCaP9Ik7vQlgapSr/ADzKLIDvD8jxKxK3klYm7MDU3YrxOCZWIlQGWIRpVTiaoLyag6/C9zcCXxABtcwVsY6CHTMs+JVsczojAVp/DGvyKi6bbNRm1Dg6y83QK6Itt1XprcTZ+m2gggAPSZaBe73jm7LxGwvfgiDjHNTNDtusS9AK6EO25VV1itMHZ+p0nZpeZnPZIz0lL26JjXMSLjwuJzF7ZQLxNtqLnTsUduCMq77el25+bT0Zc7ZzmFZTc2Bo4RYS05mLPZEOyPWtxBzV7y62yjRGCdcxoRh5hqGBMLMsahXQYziIUK+rlu1t3KFq1qGhd+YtyurmK10cHvDK3ZJYJNjhmMtPPEpj3zBtC+hlMgHxde8DndFLly9u0CWX2YK6I0ytzme6rLEKsOLjw0h42uYA0kpYDiEP0lpdnJxFAbxyfuz9Rd0oNgvi+SN+4b8og5QdXiYskvmYtFLwSomLB3qpy2DrpmRAGVlniVd9HeWHc2mA3Ua0Di1PwSh9kuFhFu6+h2ZWq5gHwSZQX39K2xuU4NJTxYreMS7iMV5lFheI5zRuoMoB8JlYx4JRlZlyHmWXtGWpdmlxLDgvmOQUx4tiYVKQ7cIQ72ETThbTLziNR7Ny9o+MMOVaLu9w8RGuiF2IdQl7T8y8wQYnMy6RoEtS/WirtnBBzzOMR1iNovd+pVril3dlcd49sJTMNRx+ZQntjcL3A45RXFLrmW8R4RerczFBjiWC+iG3CFJEh0Dd/UE0p38EWMEyU32iC0smQn+Qg7QSMOXSBLC8l7hXhCMjZ5FJCOprPCCGhQtdzf3R46xdNeE60Tc+Quo8cxqnMGDbiZFMnSXcscjyjWEHqy1MtTco+hMx7p/MyXBtXEGi9RqpYRmeZlWZmz0CALXUvLbzLOcp+rTXUBOAibwjXTkdImRNy0ZfrFubRzLpmLhp9GhhAoi63KAogn+WG7HrBeLiD8I9AXRcVaPJ6nAB4n9/I2MRyxgd4vkdzmW7LjeZioRegXuBg6FXVXDjReCGEgOXcosOb5Ja3Kjqeh8/aNlN/iZ7d4lqHaMYRLKlOKTHiLCuFzTE0AlKLmVNEeLH1tc8S417kTJDVP8Acvyq5auo6zdQ1hBHTD8kvZf0gjdFjnSCveLiiKxA5m2MrDqUpRDrE/aQQLCO6OAqO7lc6ulsAsc+JUNXp6wIjoYlX/dEwlWE1HgqG4DoW5n4lzs8uLC9TvDmVbYlhnfO8y2b6V/mNxBimDRz6WF1FDLGrrcKNn0uz7BiAdJG6Zz1nIlorRzN6iVa38DLJ33ufQ2QC1wQOl1Ok6wuMNUTOAHNrOyYDUk7ENMqgHB3KAXFg5MzyFyVciiyGzAg1LIYPDLcejxBUBNPBAGi9oEKFS3M/EwWWvf0QDuIMawP3EDBVmfppGze+rOVtvcrEqi4NtRYRTuKKb+xijZubTzYq50jg8M6fCMVU7Evw2qnvEzia3886DA/nDOyWxMDyivCAwblMECsGesK4DYRcROcJTJhxTFyxcxwt6ymm38S9L1MwauWFLVbgMRoCUPJLXaz8xCYQwkYTLziX19BQevVTzHRGQ9iF5dZS3Vv6c7/ADnkZeaSjtYiwYjXYxF6wVaO8GjCVPJlNX8521fEnVr0lDKNkxi1l1CRbmTfMobyS0I4KxNYi8oDhcC5XfvKI7GSOlkweZlio8qEv7gwV67dieWel5EsZLekUN7jrOYWAZgXbxKeLjBHPMu05etP2Ya1BbjwDLpZqO4ij6O4MWldwlbncmcXZELDbmZMoGiMgLdzmiLxCI4VCsVavaExcdoB3ZgKLyzPE/DXNVapaAz1jlt9RQiyw0YgG0O6gweuK/T7SKaaj3zIiz6lNQFpkv8APz0rPOoWmeGE4bmLKXPKXUGniHUjJ1ppOmVag0XBH8K4gHBHNxD9BiI+43XxULDQIxVfCjownXKc7PwSorrEvez7UY21MTkIKjeI9HEZMrjTkJhVOfT5g1qK93oaQO5lOCGG/Q3z6Tph3zapVHZGuiWLR1MR3wIjHK/ju+eEFh17+EqeSG8Syj3fg5lnxHD9r5QLxEco67S7xEfn0D8u1OpHUDMWmL6wTC7pmw2E23iU6zHWYPpG9wMqKGY5IrtAIeT5K3Y+AdUsF6mC/UMU9KBOv2sLaJQmDj1I+/EfQLin6EfS8elxs3oWgtQxSfyfPdKcxLVxgIKaxqDnAuVOL+1VtfSLPw2gHLK6TZbMQLwY9K4lNPzQMdYbzB09K9EuhtncjADUb6AA0zCTnlVjxNvoKUda9KdVNqSPATsYo+zWgh+6JWPTj4aYdU1NYNnfUl/8078IuYwOfT+MRYYH6IkPOMtVKzUkzeZ0uI5fW0DO5ghCclEo5Yt+In2IUbNkwzfPoPpxCU7hMoKxzMMXGrDLtPm7fHrfpyQprcRSBGn6LUA5iuBl/wDpHD/cCl/hFJfoa4CNVmYt9EC7HHrv7E8C4NRNj0MQ5ErHeVowRMy08RH2fOzuEJKzKGBBmbnaXiGn6XD8fS7iW1mcgIUfRan7ONNkQ0Hwgmg/si2sRwslELiehzDLifsfOzOyVm48+m7UrGNwPM1c3YzsAtEB0x9JtqM+SJidR6MmXYiXtzO9fi49axbr693ximXC1j+o7fMD08R9Ddfn0vAx4MDjM2wy1hzCjcaIEVW302vtOD6OvooesG9w4lHESFEINS3CDTgxqd/AF6+xgH5cRDvbE0S7qX1jjAMs6CbnXz0gjSQszZ6Tg7QYZHZaGPXAEsPojQ1iUMHqxi4wT2CJaou+IEjEWfgW9/YqpeVxfvNp4mYTE4fRHPbYJWzWqclzmO17+tqq2vo/4tO6VNxx6JZxK0Cc5ZdaiUag5+zebib6lczmaZmYWR+ipOHMO6NbjxUubPp1Q9Jjm5htBToEWwHvDGC+8x0IWPCsywQ53KFdI8zJX2ULaIaoaJ1PoDOWonXTpEsfpA3IV4VD60dvqa6jEOvvMNp0PWxCkAbPXC67EsbJlLi22/ElfX/qmUm8QbGq6w7n4ma+7UR4iORDPE8RzXHx2F64hcs/XfzxDRjZfqLsMun3pXLU2fEr1iWKDcW2365Chg3BglFpdb/oFDFYfX2ALzHwGGIbQY+zBWg/fBrby+0qnWQWHpdOr7LWN/fElF5hbv5C11R/p6vWwX/T4IOOsblQ3YuyXAYNdH9QGiLctEXawxgp/wBQGEBUZ4GmK+svyOgR8pnXCbMFywYyu8e7bOIhh4Y29P8AxuO9+KOgylgnoJULehMyYZX5J+DcMxFer8iHNn/3rA7h9JONX0vH4m9HKy5fn+iwl4r+BVyjDzCVFNc1jWCHVZMKy24Nkp3CzpmsIvS3uzG5z0hyF5GdYjAXHgDsSU9H1paa8/VArQXP/GiKpKfUtlDvcdOUOVWD1goF3uc/S2anZCR6PZyOL5tjf6S89yIVfoxb32CiKLYOCrqd7Jy/gJfPbyWo8cHh/THWvO55i8ATZ/GzmAkB1y/MrubFUQdD41g9eLXLK8hQyoY62X6SjpMdYvEubz9CpmFPyS03TuMPvzaPBe6s+aPr8UArLUE1eR6B/dHRpOUPWc7953exlMWz1uyU25GF097iSgap3Dzu+/3fpgP1WItcQ8MfwlQ0/csNY20YBKgT12g2UrLfW5otdC4WZMLRj5KdOuxLLPKDNQfrRW0ep9Ofq7mt9AinR7mY5XL1Q6vAj1P5J/7BK/8ABBX+Y9NM7p9oboHIJmrHvP1nEo6fQGfjQSkEjt2ev+Cfzz3OIPsRwrr1h0lemfRCgbnQ3M+fO/xE7TeQ+6dl7yok7QvGlztF8f8APvH+6Yd/U/y/p6u8s+Js4uZ6TPT1sNso6ZZ6ePqKlfBXyq9K9KlSvir7DUqJfEB1U8Jn5dBr+m//2gAMAwEAAgADAAAAEPvvvvvvvvvvvvvONPvuMMMPvvvvvvvvvvvvvvvvvvvvvvvvvvvPuvvvvvuse7xz/wCPvf8AHR1fJac+UWux3fQG0B6O1/8APvvMPNvvum7+/stNoo23TycyJiEC+/3FOvvvBgiT0hBkoRrtgr5MrEsd6Hl0IGT979w+eNEtTVelmm3XHpdNOO2dZXDR2VcKw7omrtNABHJOPLAFLnDE6qPC77prMnvHPPvvu+ydoKpKHgOCOs2IKCoqp3HqupPDTfvvvvFucJ3Q3Zx76fPvvvidyoV7wSlkOAv88ryfV/n3vPfLvfvvvvvvvW3IoYXVeL7HPvvvq2XomC21gyps+3Bwk6ZksnZfvvvvvvvvvvvHRqQmElvvvvvvvvu+u+q8jT5PUx6LnlvQvIcffvvvvvvvvvvvvrT6U9oPPvvvvvvuKWoGyFqxSeDEHm1gEiH/AFz7777777773777743hG/olL7777768D6OGHnkgiRx/u0BODpn777777777777777776x+n73d37776z/KgxHlFD28774W/zLJ+3/77777777777777777776/M1/b7777ybkqtRD9B7376db6ddUmz7777777777777777777W5hel93j7777777rCvy/777z76yRFLPzb377777777777777777toON1Kz/777775WR2/6zz7z777jdFvVGSnT77/AO++++++++++++gg3ucv++++++4qLngm+/8AvvvvvvvS48M/Pvffvvvvvvvvvvvvvvu2v/1vvvvvvrWJnHI7PvvvvvvvkSyO2HXVLPvvvvvvvvvvvvvvmWo7fvvvvvvvgv7/ALz777777767QQnFlf77777777777777776vFs/777777776x777777777777z39+2Bz73Dz7777777777777wOfz7777777777777777777777777773762/wC+++++++++++++vrc8++++++++++++++++s2++++++++++++8++++++++++++++++e+26+e2+++++++++++++++++++++++++++++++++++++++++++43uu+++++++++++857ww817z/7/AO9+fdPPvvvvvvuff+db/f74XyfvvvsN0R/z47wwRTzbewQQQQwxSR0QT78EcdbW39/v/wD/AHzvDPPPBRTTf7zX3bPPf/bzzzDDHHLDDTDLDjBhvvzTDzjb/PPf/PPvPf8A77//APP/AP8A6z/3zzz/AP8A/v8AffTzTSw249+//8QAIhEAAwACAgIDAAMAAAAAAAAAAAERECEwMSBAQVBRYHBx/9oACAEDAQE/EPu540vBfClKUpSspS+hv2mvNOCYglneWylKUpS+M5KLNLilKUXnfCixCEzCEJ6MIQmYQhCExCEI8TmhMITwnDMQhCExPBI7DUNCr0RjU59wQhmnQ02USdjfChYUTGRiGtCpdCgbIvhFYJCINI12V52REJBlKN8THRRHFKYtcIDHsWjZCFg2bCeHzIQuhil4vnYohs7Glo1eyLYTUFc0JYU7IbQsCDw36VxJlC64EoiHPg7E4JbGNDRwScGhDl0QO0UtP8KUvqU1xOk7jSlExMbKbPwhs8voeExtNe1UjvzovwyF/CmMk8E59YzHKG2+BMvroanhBLXEmN36JR6ZDrK6L5pcGiYmuNMak3PXLwtGb7GgmH1wpUEoiN9E2J6n4ITiqiRvjaOksgSfDFvsUfpI+BqMfEm0Nny2UTaNGyvqWIbv2CPgX86mJ60J/RX/xAAgEQADAAICAwEBAQAAAAAAAAAAAREQISAxMEBBUVBh/9oACAECAQE/EPTpf41RSlzCc0XOysV5UpcUQvC+ClKVEDeVwpbicUuLKUpRvNfCENm+CeFKTCRCEIQnCC1lDWEiDvw3ilw3wmNEJxhCYhCYhCDWIyELBsuLi8E+bcR3xsXnS4uLioq5TwNUesLiwoylwnyQ0KkP4EzFoTNMEKVFy4OlQqK/o4VFwQ14+opRqjNB72JEQWvR+ghOTRUpqDHsf0hrVIUtHtCl2WmN0SZ1tsaG42MQNtoV6F+h/i8rVHCmNREEiG34UTWxtXsr4jYpFCGoStmzMQT/AEOIaRSmxIPsa3or4I7X5mqphhcJTZ4r6Q6zpokFKUTrpjaaNOk2MdhpnQw4xIiw0KhISnoQixS0PeIaZX9E0+bNalfMWD2Poo8dEqgb2MMRzZTaJBJvovCVcFQkXqNheyHNbG0hNPrmljNCEuCEtMX02dZmTopLH5kbDVC1Cd9X/AzsNjAkfKXR/kN0rhBrDHQEprKOwjSwwktixCBGn6rRUe+80koxNPrn2LhyDWiEdOdkNuMu7GjHeCR+mlU4XCbT0VN8kuTPrwvaoggmPYypafptbqLilETe+cgmUojtNkXiYlPQnomVDReDc9Oa+iLfOSDeO2ErvwwUazRqE76bVvC2xYvDNiRPb8aVQS+EGmsL0xoqfRJvoUt+JPYSPKrUSIQ+xqdQ68FtPP1Em2IXuqHljcEPOmjZv+lH9KlvnpBUXFRGSCoggqL46ioqKi5KN4XfG4UrKyspX6dYm8UpS+elZX7lL/T/AP/EACsQAQACAgEDAwMEAwEBAAAAAAEAESExQVFhcRCBkSChsTBAwdFQ4fDxYP/aAAgBAQABPxD/AO/2Y8Fx6ke6zteus1fE+7EiIUJ6JEpp+gF1mccZeRxAgtWgtlg9006RMUbL/wCGIArfJSfMz5XVdfMRNif/ABt0L0FxOm+iESKJQAX4u4+UubCMc5x4lmdxgvWlJAW+kyDuu7T2gKhUMMvPKMqTgpCvZxKzo7owH3Ipr3Ob7wurDBj74JcUtjf2GfeBnadzzdnzHbW8WJD8wS1S1S1x4gjL3kz7alTaCg7eccxpbLZWiAJVkN41MBjTcK4Cqqp76n3xf4oUUcbTI9toOfLp4FWD/tyib80mPQs/mCAzul2KJ27wv72ZKrPmJsfQCoG2IF2tPOZwfRQExenMJca0gDF5LxKlOl2n7bmCvmVvmlqdYqZtQbj7xLiUin1q41hc1TX+vvASBo9fi5RLXC4RyUbRZd1XZKSALY1HvhL3gxxHDAXUZGaHrFXaAuhiUVty0sT5CxFXRV7BbH/L20MSCB6sp9/3NPT9QbhegUM+WdaK3uKwL+ZUflsrfJaDxdTX7UDZ5S3ggW5G0ucYD8xYANhFnVs9yNlgfu9v43B2slBzr5scAyhTGduhy/tD41QAvdS9dr7QaHuUieGMHqR3bVxdIMBxAE2+l5e0Q4hKsLdz8IjsG3PHSiodcGLrX5QpVDAh+bnXqSjfdL6x6adYIDE0CStFkyxlRvLtAZ3LdLb8xUxiHxUblV9CElI2QOLRQqu2MTJgY1v7kqHy21Suhsz5hy6ptu/Uf1Kcgs0kWaz5lvSUrj0WbJY0Sy5cLFtpFfMRa8oB8rEqQ/qi0IoUhfXcXEV/BFOKgVQPhG1TYwSddBcPYgFNuCzxeUPTioEdPPLo0B94bu01fbaTnBxQ+KpFHB+yECnUAR6WfRS6JcaceY6jULaKqC1YXVI+OsqbS2JhhHjY0jUN5eKbIoLT66aumIUoV3IF6PmNcj8YvGGYWzPaKHBiImz9AFaJlulhE6kqV3PaSfg/aD0Vwhm5rL3gRqQTm14LH4imzbepG1AtPFxQDaWStOzPtKUxsbd5gpfzMYChUYrTn+ZUaXgrl2BxHvzEuHK1FHiLTQygTo6+MxaIMIxehQ+ZzRkveVsFarkjZyfx4O5vxZST0t6y73Ekfa6SO9DYLe/+4Iln0XLl4tlqFjhG4BcLIA0Oqfsg65LABStW05qPkt2Zf6AxVV0o45lHEGmKSw5mby8w04lPpcquaLXQeYUOWwF7Xo7y2nFAYH/OKjRO2uy3Qw8Uklkv494GQNq4temMXFrOtA/lDUZtAL3ORgiVHKE/CGFZKpl+8DgA8SxjA4yJv27xraNrSPgjogrTrpbmXpyAfJYEw4roSx4S1dYBfYFn8wIEPOF5uBT61VM9RzGbghQeDVfMKhqMtWqM6+epNDCoYBwKRMiXIqzVET4PEVTxER7c9oBXKAkPulxKPhSt3R7FRG+b8ynjMWrjmLBdnS9fMRzUNFtR42ei5v2jQNZAG96ipbEQ/Q7X2gfbi57Bywm3OqRPzDTVG7ive4LtVYW8M7iK9eIP4gK2GbrR8wIepAX8MCGrc4ntucWKyqu1VDt60Kve6iwKI+0tLH7RVKaNu3Wb9icMkB7wNU+IbClBx62Uv2gRUYf8bHSGvAqEvwczJyuiop2aqpvOLQhfaxr5gJtQe8NFZjep6DzlH8ylZsUPAMdYr3SNDlnZfxM037g4UusO+IRu4oAC6xX81MeWNgK79zmrO7MZGwH7mcTWqh8VUBudWF+PQllYhvIaxH2IEXe2o/LGg1V8elT3ihZBF2Z+Ijj5aFh2LMxszC8gd2/vUW3HrQJRvl4+rxAs50BCWL6bj/6hAvGiov8AUUNCA8ggfzLWc5GWuwfygoANFI/LLOHi0Hwxnf8AgQR4+0jLwLmVPSC99uOJrh0+nZH7QVmXIUXuzDfSOuwwL8iUHDVsTdbLA5tjA2oDs4qDetVAOxsnzuXeCKDowZB4lMBNhaup0Y3ijRbbFSKtUjuCWpVLIea1FRSKdYV5HgiCiDXROhLjKEvi1PaagFxJJaUR19kBmO6GUonZTMt2qQFdSNXmo61HihmWEopLiKUh24DOHv8Ay5iLWMXob4xMwl6HcGmhgXtDH5jv7ZcOo/1AlKO9P4gjSWDD5pq4cOYEHcVdkTXJbb8Jwc8REM3Kw+TEPA23gvIGYz4aWfiWvvF0HQw8nnjETTUepGyk/CY8S5ChZccM0yC6kcZK/MxdpK9oo6VCGFvmFQtnBTUCBbPAeAZsshbK64e5Iw0DQ0PJj4jsDkW7488wxYGXeLEgewVnA8mY2IxQH7RUUaoyO6QGsGSC01XTmKhGqYNFUHaPGE+ZQ2ymjjIYxwy5ZYRt9y9Y/jbgLwy+6QkZu/TOohNypRRoVV3fMGgGwLdZXURVSAsYvkr1ZaxXrK1Vy1bR8RY+DeSCHgEWrN9jESj3g4djjzDEJW6VXxG9lfMLG9ZVV71ogSpimSXXTHMQ7w9yV1rctxCrBp+t5g04OyQrbJvJeWV1RW7KEixmFF1vchVrPMogum7z8Sy1ZlLvak46xETngQe7FlaxtTpcB7MU6roWe5f+5kRZQJ7aJalBAwQYC8ZrBLyx4uc6stE2gDhtxEITTnAPF0ahq/lLBdAb94FlQUZXURxcNXYuNAEMu4BYoaoibKpn5mMFOsYWWlubr2iFWGuWEXSPeWOxfaJBcIhoBbOvU8xDg5qPcOoU2o9TEZQuWE2l8xTIq+sFGrO0KGwPEKrIjZSL7kF1PQ7kJd8kpeI5vEamJwVaw82IQSK7yJ9o0NXpVL74nLNGoOg3MgmssuWsQVzHJvwgUqhlT/F/mV7a5aX8xnyc4PN1GNM9AGEUAci1MgW2Gx/qYrvsZl6zOciUBYVbHiOqK3LisyteG7FQ83BRq5BXwlnvcdlu7WO7R/MMLBK2dwzhK9jq53Lo7Cq4OX/X4jZkALaUUd5S2gSzB8c+0thfTG3uUuADQyCa1XDpEtKy8SjktP7g2kKyBvOAouIqbRKPC/zDRmxCdSoEH5QIDjOD2JHQpZVHzn1wgUWNFNe5DhtGXh7Ii6EshLAVRe+aK/n6NQuoVZC+UAJv2mvtL7HTS3mtxVbW30FhY109LiUJStB7QCmUAXfzuAi0rSvjH2iFoG6QsgEAurzc0Jf/AGa9o5MAGG6wwgtpsgTiQw0e+/nEtirwj2xaDMSKCIorp/UKBU1mD8naxEjK0BrywvUSr3T3IVYbsu7Sq/7vCLaeAO1O4UEEBkPBZefvEA6nAhGzoMf9uJBXsGJZUrzdEI2bi91EcWfsiMEdcTCr+5LAUjwwAxLxcLC2rpMtZc0QBSzqyoLvmo1228yhXns2JX0ukB2h3JUgO9xBgPeJVEsvOIsy2poZYofMVVNBxUaCFIbTvwFjylWFU0m2qu8stW5YKlJ3j8QZfAUTLP4gYaGnXoisjZeCJgiUGjuUkUdSbqDRhqWKm5Dcq+YChtCBVpoo1FhAeTb0Lx4vzDmnSbXnd4qLgr3h/eXxM5VfuQIeHUo7VuUdDyRAWqv4rzKt71wWKYklqcPaUgt5YJDoAHN9IXzZbRl1pmfDKKj7Tu0C1+FGPeN1JSqa0+0AMUqrlV87reIC3aw0orghmRLHJbOZeLdNae52lehVNjdYlSXhw8ap/wDJStFUfvT1KGuF3/5DlnZ9hBojyFVBav6+gAaUqpPaUnkbZfm7jBEEeV/S9/BvBBdN96jEvVJddtEomjgDR8w3kllr33e3xE9mukWvNJxCqmh5MFEK6WfvV8wyL7TCE49/DHkKDq60oMS0GlVcK5lTsFC3uNal1oNMMlB55UeJWidDqi8IcF1KawzQLiy1BabbhBWjLWvbpACSGAq4+o4AuiMAJ0rKCYeR2vxLqxqzzAGhy6X4/wCYcKJXD+UtcYurMVUC4vacJeUuMNvxcdA3V0E0wV4xMj2YmW5Y5Q1xCRI8XBqGXNV+YgGZ1yfxKS6+Ja3AMqtS/QsKpeymZt3744fEWwqYMvszCo0UFKt5cxOv5JQ4waiNRhqa+H4la+ApOezHmFAOAwLd2HOUBe7Tf02wFAFXpBbacuAdaZn/AJioQu/mVnRAPYEP7FKqPb6RqQKLNf6/qYDOKdKtDKbENAZPHx0mdpcroxqd1GqezzFRYCmR/wA5i7Q6xwe8qCMXSjLBBZSG3HO40LQOFod/gjJPO6HtLSWmwwMb7CgJOlwe13dXtCM4upT7Uyw5xSFqrazEYAEel4XUxarulHuzNRF12qGoM3oQ7wRHIBAnSL/mQQfbOH2hl0lJ0f1T1K5tnARLtLhYlOg0SuYEIYdhamGM5UYGFErWAYEtCthi85dw5x8B8ILBOreYhJWmDOGBlExQYgtlWst3GURQpw62y5Oi6VZO9Q7drAPc4jRm7y68f7hIyhsu/Mp7nCqh1q/+zF8H2tIqGY7uZkrIaag9EdyBp1chPmYgd0n9TCPgxXf5lOuBaa9uOsojoAFDi2pd7Fa0HYf7hdVNXhrr4mkIBLDGsw0yQ4d4Synrdv2RCJtwt/7zLpFDus1w1qVDAdXftAk9YpqUSFXQjiU9AdbD8RwpgUtvngiWghkAdQIE7AamPUrFaLal/hLV8tAL94bUqtUU5z2o9oDwtDY9fobV/haVXJt8CWC0X5lND0koM5ref0qSo1CKRi2WWPOZ1gCf5XARkIKUryczKpLRz2CtMWSSlphTzL8dcUm5g08a94SEjAQ+OIQMNcOSPViE5TjhMNfEFWtnU/MII1oAMxXBkIx5itZlzek+8fWkFS21mCZVqgB1xiezI58EHJWQm5klqwaNx0PBbB7Dllx2G4IVTW6w7/UqArJ0qXlj0q4cEGi4TydXBCVB5KQQsrvVTLkBl4qIjutEo9XvAgToVzQduso1uI0jaa3rjxLhjrFd1XpyfEvhDItr3xjr0llUaLdu4DhyGCANHyqA9fbNTGlC0865qHDZXVSfEEsXekiiiZshMIfdEOlSgrMtZV+IlrbIluxGs4lC4DK0vsl2cxMx0mPMNJSjtFkGSU0HFa/3C1l27RN3i4B2FvNHxACyo1E7Xf5gazi9p/Dof1LewQClVKU3DaQHKPdL+A5NPipf8LIEFwbaK4UKs7zCOhlA+MQulJSp8d5kMVXhbrJje9y4UjVWrWcRsdRcIApwm8i/H0IQVHOUyHYSB7uxiKXrHqNIpfaIaJ4OCAto64/XDJLkgEUycPwrmT5VBS6a3EAFoDA3kHsQ4RS7bvdhecy2L3UEpPaUW6quYpuL8TNPJZghrWxCdAGq1+JRnZVs/wBwl6KSHQllJTUVSxLaLwwnvK0wXQYqaBX/AA3MARUrh38xyK4ofAP9ygrVrkLyJfvMQJC06TTjUTK5JBd8OC3oR6Owr7tcv6lRGCdiOG7PaoED3VBaqMHEFbqnljcsIDizlGBeiR2ZOb4h4v8ANm6a5qZogtcGs38Q07VUF8oLSWFz/wAh46wWW0q+c17QKIWhy89IqmxEBvdLH6YAAt2O2JU5pVy0ZysNoWl1S/IgmdwKFxmIlnP28xUWI0Ur2T3ShUwwTrGoi1tXFdxXXEpkr7EoWZMmkS+I4VQpZc+ILUqpBbNJirgu61KCukBM4CUemD+olWLbMDrzBXD4Aw53MCVsmZa69JtCsgv5Y60sbC79+OYEfdlU6ymtbL7L5m+Y5rJeE7SxDbUbp53r36wCq2DN010IChFL6P6VLLEYWCOz/twlAC3Rx6GQjvTzEhZootujg/bbruobM7YLIXXiNZku5/LcSNptSqhol5paU4CX9QHPnELMBTkfeV0GKW2cv2jh2o2qdRF4OFtQOosVATOQHIvOKx/1xQBFlR6NNX46xqXZZD1Ot7uNovYMZ7ax4lfBOTrX/ZjOPaXBtGdHVBwF5fePbvAhicdWu2IGdpWFQUKirEr3Y/pgrQL4jX/FG4BaZJVdB6Rl1W2ufPaJ4LCQnYjBhi8Epa9zIUA4LjKuC9uIozEqZGOZaLhq24i9SIy8ECtrcFJVmSzzqUEraHedECkAcg6hZEvCyvGv9xrpCkQDRZQPSqp68zHLPcfMIH5zkkpAGSUD7aZdRWgbbqPtvmBW6wmn4hFhahW4pHZmAVEcFF9zEBVyUuD7xWBJrU65INktAOT7oxULQVffCQXJcVez5j8CkKkjd11EckFUAV8RCqFaWTijghm28NgmV4ubDEp42asalqTxWjvOCGtFxHwf7gBV1gcEKYGqgUF9nZmBbYXW1OpxCUpEq1Y/cCgXljcy4mwccIdIzeXr29x01K1q9tNdncjUCmyN9ahLF4AOvxGliV5j+tt73CsSbWkVpi77z3jwto2w10e0r7GpmAlad2y0qv0rwsu7FAMfOz58xA6yqUHtMuWvEALBMV+xeLNNQ662wVKCHIswwzTOw/NXNYgr07nMot8K9oY7DS8zBFOBcj/1zMm2glWF+iIQuVtLCDWAtfIRe7jBWfaClpC7HF9Y1aqXvWAd88xsPWtg8PMLg+DYWccsRMoawunrBAFBsslFynnMUyZxKzTWXQVLIsspvySumcWI7h1jRNUKRifBBadUh0+YK82xSD5gHcMqP8xA3uVZb8Jnw044f7lgGvfcCtt7tVNJ7UtrTVdJS7XGLz5l0MtimUmTLxxGFRcjf8S8wrVNVELReqy2YK0aueYlEtGEgJxO+FkX9yoiNQDC6lcDxFlMm1BTzEC0MOq7t4lgDVJwAdPtKEIHNesZK3a5vmW4FhmSkyVnbmIXgPDKVCQOVoRK9yNzhwdP2VKw2McxYs2UZmUMqXUVDn6aqCOIaAZKXcLQGcORMjy76xgF4lZi2vZFkNhAnfgP3UNShwLJVHPQHHeO7CswD5xBWF2DF2dfNw34LWrNOHJu5kpJAnI8RAVBmqfaAAQrDt9o8LJzagNY9o6dph+A4uDMCKiHZjtKum2l5hrJEQY3sOsasCZrMrApzlDXakMuA/uO92TCsVBQinN5LmxQtFreZemvhUNbtxVXB6QRHB1LIcSp0plRrwOyQeA3m9wyIsLPRUigBywzedhbHesB7xX7fg/d2wBXDDRTA1vEQ2WGiyhlsqyrcS33hdc1CBIjwyg0OKqLWFVzLJXP8R91RNiA6EwFQ/Z0yytspv2Jaldejz+Zeob3sh1PTu6ZiYtW4fMERZW0T2lIt3cQpF0CAB2l1u+Ytug8elWBvKNHWDo8OguChW3QzOAJHyjKxZgmfl5ioLagYCREi7stgHCAEskuLpdEbO0GEO1Zl/xOEAH2maQGbDoU0fEDoQnK6MSFZKAr+39wuUBkq5ZhQmTDMTCmjHI8ynNtlMkGr02L0+IQPfWOSledzoabyuYQC5ve4l3omPnzTqGCyzpH4DVYhcJbviDxOzI6zLNr2RVVdv7xE2rZi0t7AYOC/Ewwub3uEQNhnELpVqKAUCGbEeYy9q7c1ERk7R1FU4KzDLH7Om6qG1stUzMzQDtHliDV6gF27hMmZQSnaYCyq5YqTynoO0ev+OY7sCQ2sBMNnIlf5uW3EsMldKgeC5X+0RQDq/CAAvXiLdg4TDirAzvcPXRwcbi+/eLFopMtfmWZB89IUEU7ZjOqXeahLYYE3HC38R8iEWXBDCSyO3k48kBjc9Yhcjkdy9ie40PtcTUhigh0IQHBgTb58S1rIm9doBmvAQoZGesJWuMMYrsLEUIUu8dohq1eIRYDo/wFpLLVdv8AEXW75i5yCG2AhDqtGgqLL5iFSlHppGUwUlfMpWVeSj3/AGIvsaK2rGKVt3txXiMOigIF1SHJi2P3sFxKK5pvK/zKkVd2agGllMMrarrQMseKUhaS+lcxFOGY8aCk6jOe9kDb+LlDkUdOozH8TFRZ5iBFRIu+0x6sgabab/hg8i1gJ/MNt1jGl+JR70CDmFYF07QsNSq1klghQVuIHIgy1fPeCFU0LSFQ2W7gYtZWI+HSUqle1rWJbzLIqAsJjpySvJXrHEcLhUBZxCQ2rpHRIjL0Okq2AV/gTboGxINAq2DA94Rt3OpDso1dRnC7Y9LmeksVYd1mB3CYuEUuiPsgOW7aDmIpAibH9dFfrF09XtGtsWtzHQpOVirbJ2pC2AYUUzy6girbcDAdIphKC9r8x4A8tsTQW6Dcqq0pl6RMgSqJnsWqNwT5PwHiV41RWFTpHpAmqLBnN30gUC2VuVFfTEe1IAMAOhDONwK/3A7stFpdxdaHuy3jTiyhIQw7R1iNlt3zGNZgsRn5gB0y6r8wxerlDdA8Qd1nghiztBsIrAFRY7uodDJzW5h0P3QemXI14gGZuyeT/r/bobL6w/rGNmOkoch5jnV1XrLYi9JKuqOsQSYNYPS3rKQtJTBDqsmndMdGvJbucA11/WfVjFsUN8+YUXRVV0PiLQMHHEoWF9YtUQpLMP2j0UhFPRjELlOyGepY6RAm3DFaq8oN4EtxLJqQjUfsAKJoP/kqkGCuM8naAAUXrvLG1yia2VDjDVrEhNDgB49VOZsdSiTkZiWtFy4xo6wLATXZ3jzZa0HEtqS4RYQAA1f3mSDwJQWFXQmvzJ6O0zy7VKZllQQUFD/DIrVMqTLuThEd5gCVVsWYMgHDClsanMtqr9Fvfob/AE8UvXXo6AWJ7H+5ZhZHrEV1lg3hrrBa1/cJzGUBZBjXfLA7Qithh7oe8t0VXqxxBKxdYgOzjibh7Sjilp36/NRkxBKIK35hF0dnrGR7jRCCWuwUPEEpIo4+kbLd4jayPDDEE/DESiKu19UzhanFx7yll8RyI9pfgu80S0TfR9alNt0/4k/m8GBOHI1mW49/U0FL5cBDcBANJ7LlZjjf6vWBMJbrEA8QGmMTOLKDoTTntLir4YFxW/Me82CjhlB3ebKhrQWHJHpwu+8dC4IlujXeXnDeu0cArzTAILpW3hHl6rZN+HUDNjktA/gmAJa2VGra19OycRxsRK1zKXYKPj1xzExMjBKMd4yYHCM1BngvUR0XWa9bA3dKjW0DOP8AFPTLWtamCUODcvwHvqQmqlLcY2Dvti2CTriAKSgvEoWx2R2fqIrSMJq4bgOFTpDTfeK24lqNWQFy0Sprw3KKJlIRsp4iBW9Ed2SozzLJhm+83+sLI4rmHwa8ZgakqwZf7jkpcStEsVtMm67fWBG6AF48x+dH0GIg7Go0KqescFbxlyw9CjLMtjcMo0Aum4LDo/4sZyl7QszMP8IwbeiCQrq4fvQwxVWme8rF0C0f0xVY0w2h0+MQBYd9QLBHpqYxnSCE0ANLyRFJcI5igCDlFsV7S6W4VAJWoIDm4Voyj5lhAXshS0VzZdwExhpxUrb2KPwfWei5Th9ApGjByA76kK1aRzXPppqKIfMpQmuZg1BBoZjnK/tccftWLYwBOu47BzuOeY7lsMrMF0YMCbmRxfU6wDR8l3TL/XKlOJTcqKwl9IIgjOmXAg0xiRF+5LLTjUYmW7/rFVVbXb9fP1ilYPvKpqEFKdIqGOlSiwKTMsAxRdh5Y1FU7U3cAvbPT9V3+6Gr7+vd+mJkUYMTLklY7xOkRggYmr8EwbL5goDTozEDvOIJQNOR3EQETh/VQVjgwFU2YzD8YjpMy11KbzESqrEZ9ktziMHVbGCoul/j9hUJsg2diRyg2rGCqIxhRYgnDcQsLGnWKLqvEx5BM5xKHt6yquBI4cnebIfM8wiJ9V4r97evabb6iKsO87EM5ETgjZzBAJxjEGbIwlLlJCruU0ExASGnkqUY2Gn9W4tvognRKNU2x7y9pUeUdr+cQcqp6uJbXJdVFtXr+xcxrklwUFHLuKO1e0WW7qkirYveYCGTTfMYiFDLeWG9y+FuiPLWeYKccTegb4hlw9qhIAoSh2J1P8ECSksekpnNMTrzDF4xApMxKMZYm263LPhm6gkObXEVgOYuRyh8g6RPUVinmDpWj9WlS0qo6xoe/MxBBll4mB3xXmWUDYszKBEYUn7IVWKeI3h++YVh7EOSzABQFemRAXg6DbGOCiKSuINNwQATmoTVd8TaLX2mCTyOnoNXiADNXqj9BK3+6AQWPB0ZWdnDxK9oHmFfMXRczNCQSioxoZYpQOOsZDzyzAVwznrNiAs/rJeUU91lN2WsAZa5qLusYjhGMgrzW5Y41h8wTQwxhPQ/HpYrVF9z6uv2WVRFwI6WblwvNZxA4JWi9xwpqJVZuyUmHf7Lj9ogIiNicQFYVXP7nMyCEsMD+GJbT19o7iVzAsVxA8h8xLtCY5c9IbC4Jd7Ymh+spAyh71/EYAtCxiMYg4DNdNRINljxDWJt6Sh9vFxYUmNdYiPBoOhGLbOAxCVDof8Aef2gIDa1MYHQriUEQU5YlHDcJCQ6x0XMHMMYRl7PSILqhxL+mxpWT1HSwWmt/v0QclLmt3NQ5mivCZ7Nq7RFLgQNFzCqO8wQPmK2qMgbwd/10ZLy2dPMxWQXapEgL93mKVoXC4CIlSC8cy3hduQhNMMRJa8+iDXnDtMme38elaXA8/sb0GgqoWstKYmClLccwg3uUUwJBBBhzF8O5SnJYkdNpw1B9BjtNuDrKT1tqrx+/uLkFfSpXdKWX2hQdXcxgKToQAjepSuFZWCq6Je1d5gAKhwfrqEQInETBUDdXd8wGzX8QuYsR6r4iFzVZMQVgWAvfrY4WmXvLlwY/ZWXqFDu739o2bHRI93wcxuGBQtz1m9XuLWYz2YRU6Q6pBDqYslYC1h5i8Cd+v0IrVv+CJVlvDj+ISQMILv3laDdJGi35RAKuLQHUvRXrA/Y2mKA7zRpmujxjZFUMuvEPJBf2iJdStXMz2Kr1BQJbBw/sy4hFK+ekaobBTcac/MqB1RepqAFSvkiEWzL0h2KacsormqGmlHzLqDp1/hmVCgBDSsU6GCASVzca8ZijtCtZxXMVG3+yfJ8PCKl5CWPDN9KV3Mo65zDS0cp+P26g3aHEpcaN/eBYbI1b2m6eo6wyyjyj9kOir+YnAoll4HxLGp8j2i4ccHSFUXarfzUW5gHmWAOLuv8KgBazDjZR7SiZJVKzc6Qcxze3lGrAaP2ghgKKKpxuLNdBmXpXYZ/cG85JVKowEZo5msrqEFuqjTKldI1fBg8MssNi4308QCg3kbdxFACszHwg6OYzpa/U9V06ev7+tFaRZFU9+sHIa6ywQ5HSLMF2XEWtX/llKWu4A29q5gqDZswjWg2fXVi9SUhlbJ0/fVtKLYF4iU+ksVVQ5dYkHraq/8AyVf+ZACdTcqENg19QilTmZgtV2GIyWra/vrYXKuC9ZlB9CC7Aopqrlf5q3r9Wfx7LUDBgpQ1HLb+/M9oVjI3t6kIM1R0iQIs7Pt/hhrkA1/nKgwQN3/y5cu6G2peHHpq+8v+FQBp4/zY02SojSrBTwzE0+P0LELbNj5OYrboPH/zwm0FrfqgIKeiP0jR/wDK8fSEEbr1ax13xK9RAJK9Dh8xlsiClOt3/ESnd+goV/8AOBAI7pWu4xmvLLpnWewecPEoEGVtO7zAA422TJl29fH7wUs6Ur8S2PNU1PNypgAFCDmkv7k78HSH2ImSOi9fa5Tjepn5qdd+68R+INvxMVRwlP8AiHmEWI0Baw8LUS+JZl7S1VcA/d/qDhlytx4KqEUz91j7QT0QOMB0G5SznXZfmr2iCMailjoOvaCTHYB4HNRZZDV4OLXUU8ahfIZbkvRRPFbg1UYSE6l4+bhVZCqArwjBC82FE+N/aDmpKllzVl1HBdrLHGrrJ2jrehAv4SCQKzaKvzFNTEQt3XiUU52hZZ9piXAbx6qHEC6/D91akugXLddTaOFBsSn1AlKttufYhCVA4DyamdQGEQL/ANUavjLYdLQZggRCp6j1uCwNumrfSm5FnArD5r9V0+Mq4B5YdfuSa9wjMG9Ff5gEgdReefxLzCKADpMpe3OGvmCSx1ApVsuy3lD+pgOUFWvuLHXX6WKvtCMur8DtmETZ5Lg97iPNaLnzvh8S9ytgOXyjSwAoWs6ZIbJ2Az8fRcv0oNQ0MNA0+YJTg2nuq/qpi6VWNHv9Vy/S/ov6120X4izaLZSmA92Nr7SrvHI5jw5v3nZyUC+2ZVgc7z2aiMO2UXX6u1HhQMs3kCnhbhhbvERTfwv/AFEFD4Cv+CD7nRfhxLX/AAvDGmlnuPMAWOtND2oY81sp8AGPEf0m8CvjhiL/ALc3wigenWt4GukRAL2wSQVlL/aoBGLppPvAJ5CfkKzLJtR9xUId4AF+9+tsDF1cfBNxgEELS3z+j2/IywmxNGQ7hU8HUTB45952vIh/Rv0QeJg/SzAtl7gUH7m4lUqoh+UMe8DuI3P2Yljmm9dOwTeiV1Qj9fIP5g+fx/dEDP8A2d4/xG0JAd9g+WUaPeaf4uDgRrlHb4jD2BS/tFQEFWFv21GVu20wfEAMAPHpf6FeniVKiRUvJ2SvTHpj1uTHCXEwC5FfcRpb7QEHyQ4r3B+YL+IGpe7r8XC3qubA+ajXeXpf+obPkx3u/wBR+03i0PikXBSnKTzBPW/W5cv0uY/TqV+wqV+kygKNvIqxCpDHUKxhitXUlF9sk4B7yOZfkP5gApPNdfZK9KlSpUr195mpnp6V9B59K+imV6ITHpX1X6EuHiUyvqxLJZ1llSwdQb1G5mF/VY69a+qpUqVKlelfo49b+hzsXzAGr+WX9CbjDC8IN6163Dqp7zN69HIAgrA+IaJjcEdrlTtK9LhmVKlSvSpUrt6V6VKlelSn0GV2yjpKJRKeniZlSpUr0qU6ErtKdKlEpxcp6KPoqUgVo/ZVKlSpUr1qVK9aiHZG7ZVmmo+CEd4gWiA1kpi9UgXLSqzjMr48z2nt60dIg8S5jpEHcAYV7fTf0+09v2Xt+hX1+09v2mfoz+nfp7fR7S5fb09p/9k=)
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Modelos climáticos precisos desempenham um papel crítico na ciência e na política climática, ajudando a informar os formuladores de políticas e decisões em todo o mundo enquanto consideram maneiras de retardar os efeitos mortais de um planeta em aquecimento e se adaptar às mudanças já em andamento.
Para testar sua precisão, os modelos são programados para simular o clima do passado para ver se eles concordam com a evidência geológica. As simulações do modelo podem entrar em conflito com as evidências. Como podemos saber qual é o correto?
Um artigo de revisão publicado hoje na Nature aborda esse conflito entre modelos e evidências, conhecido como o enigma da temperatura global do Holoceno.
O autor principal Darrell Kaufman, um professor do Regents na Escola da Terra e Sustentabilidade, e a pesquisadora de pós-doutorado da Universidade do Arizona Ellie Broadman, uma coautora que trabalhou neste estudo enquanto ganhava seu Ph.D. na NAU, analisou uma ampla faixa de dados disponíveis dos últimos 12.000 anos para analisar o problema.
O estudo se baseia no trabalho de Kaufman que foi incluído no último grande relatório climático do Painel Intergovernamental sobre Mudanças Climáticas (IPCC) e analisa se a temperatura média global há 6.500 anos era mais quente, conforme indicado por evidências de procuração de arquivos naturais do passado informações climáticas, ou mais frio, conforme simulado por modelos, em comparação com o final do século 19, quando a Revolução Industrial levou a um aumento significativo no aquecimento causado pelo homem.
Esta avaliação abrangente conclui que a temperatura média global de cerca de 6.500 anos atrás era provavelmente mais quente e foi seguida por uma tendência de resfriamento multimilenar que terminou em 1800. Mas, eles alertaram, a incerteza ainda existe, apesar de estudos recentes que afirmam ter resolvido o enigma.
“Quantificar a temperatura média da Terra no passado, quando alguns lugares esquentavam enquanto outros esfriavam, é um desafio, e mais pesquisas são necessárias para resolver o enigma com firmeza”, disse Kaufman.
"Mas rastrear as mudanças na temperatura média global é importante porque é a mesma métrica usada para avaliar a marcha do aquecimento causado pelo homem e para identificar metas negociadas internacionalmente para limitá-lo. Em particular, nossa análise revelou quão surpreendentemente pouco sabemos sobre o aquecimento lento variabilidade climática, incluindo forças agora acionadas por humanos que irão se desenrolar quando o nível do mar subir e o permafrost derreter nos próximos milênios”.
O que nós sabemos
Sabemos mais sobre o clima do Holoceno, que começou depois que a última grande era glacial terminou, 12.000 anos atrás, do que qualquer outro período multimilenar. Existem estudos publicados de uma variedade de arquivos naturais que armazenam informações sobre mudanças históricas ocorridas na atmosfera, oceanos, criosfera e em terra; estudos que analisam as forças que levaram às mudanças climáticas, como a órbita da Terra, irradiação solar, erupções vulcânicas e gases de efeito estufa ; e simulações de modelos climáticos que traduzem essas forças em mudanças nas temperaturas globais. Todos esses tipos de estudos foram incluídos nesta revisão.
O desafio até agora tem sido que nossas duas linhas de evidência significativas apontam em direções opostas. Dados "proxy" paleoambientais, que incluem evidências de oceanos, lagos e outros arquivos naturais, apontam para um pico de temperatura média global há cerca de 6.500 anos e, em seguida, uma tendência de resfriamento global até que os humanos começaram a queimar combustíveis fósseis. Os modelos climáticos geralmente mostram temperaturas médias globais aumentando nos últimos 6.500 anos.
Se os dados proxy estiverem corretos, isso aponta para deficiências nos modelos e sugere especificamente que os feedbacks climáticos que podem amplificar o aquecimento global estão sub-representados. Se os modelos climáticos estiverem corretos, as ferramentas para reconstruir as paleotemperaturas precisam ser aprimoradas.
Também sabemos que, quer os números subam ou desçam, a mudança na temperatura média global nos últimos 6.500 anos foi gradual – provavelmente menos de 1 grau Celsius (1,8 graus Fahrenheit). Isso é menos do que o aquecimento já medido nos últimos 100 anos, a maior parte causada pelo homem. No entanto, como a mudança de temperatura global de qualquer magnitude é significativa, especialmente em resposta à mudança de gases de efeito estufa, saber se as temperaturas eram mais altas ou mais baixas há 6.500 anos é importante para nosso conhecimento do sistema climático e para melhorar as previsões do clima futuro.
O que não sabemos
Este estudo destacou as incertezas nos modelos climáticos. Se a interpretação preferida dos autores – que o recente aquecimento global foi precedido por 6.500 anos de resfriamento global – estiver correta, então a compreensão dos cientistas sobre as forças e feedbacks climáticos naturais e como eles são representados nos modelos precisa ser melhorada. Se estiverem incorretos, os cientistas precisam melhorar sua compreensão do sinal de temperatura nos registros proxy e desenvolver ferramentas analíticas para capturar essas tendências em escala global.
Tentar resolver o enigma da temperatura global do Holoceno tem sido uma prioridade para os cientistas do clima na última década; Broadman se lembra de ter lido o artigo inicial sobre esse tópico quando começou seu doutorado. em 2016. Todos os estudos desde então contribuíram para a compreensão dessa questão, o que aproxima os cientistas da área de um entendimento abrangente.
Estudos recentes sobre este tópico tentaram ajustar os dados de proxy para explicar suas supostas fraquezas, inserindo forçantes plausíveis em modelos climáticos e misturando dados de proxy com resultados de modelos climáticos, todos chegando a diferentes conclusões sobre a causa do enigma. Esta revisão dá um passo atrás para revisitar o problema com uma avaliação abrangente em escala global, mostrando que ainda não sabemos a solução para esse enigma.
O desenvolvimento de métodos amplamente aplicáveis ??para quantificar a temperatura passada já é uma alta prioridade para os cientistas do clima. Por exemplo, o laboratório de Kaufman está testando o uso de reações químicas envolvendo aminoácidos preservados em sedimentos de lagos como um novo método para estudar mudanças de temperatura passadas. Combinada com a nova tecnologia de datação por radiocarbono do laboratório Arizona Climate and Ecosystem na NAU, essa técnica pode ajudar a determinar se o aquecimento global reverteu uma tendência de resfriamento de longo prazo.
Por que isso importa
Broadman, cujo trabalho inclui foco na divulgação científica, criou as figuras que acompanham a pesquisa. Esta é uma forma crítica de comunicar resultados difíceis de entender para o público - e na ciência do clima , o público é diversificado e inclui educadores, formuladores de políticas, organizações sem fins lucrativos e cientistas em todo o mundo.
“Uma conclusão interessante é que nossas descobertas demonstram o impacto que as mudanças regionais podem ter na temperatura média global. influenciar o planeta como um todo", disse Broadman.
"Com o atual aquecimento global, já vemos algumas regiões mudando muito rapidamente. Nosso trabalho destaca que algumas dessas mudanças e feedbacks regionais são realmente importantes para entender e capturar em modelos climáticos."
Além disso, disse Kaufman, reconstruir com precisão os detalhes da mudança de temperatura passada oferece insights sobre a resposta do clima a várias causas de mudanças climáticas naturais e antropogênicas. As respostas servem como referência para testar o quão bem os modelos climáticos simulam o sistema climático da Terra.
“Os modelos climáticos são a única fonte de previsões climáticas quantitativas detalhadas, portanto, sua fidelidade é fundamental para planejar as estratégias mais eficazes para mitigar e se adaptar às mudanças climáticas”, disse ele. “Nossa análise sugere que os modelos climáticos estão subestimando importantes feedbacks climáticos que podem amplificar o aquecimento global”.
Mais informações: Darrell Kaufman, Revisitando o enigma da temperatura global do Holoceno, Nature (2023). DOI: 10.1038/s41586-022-05536-w . www.nature.com/articles/s41586-022-05536-w
Informações da revista: Nature